Adv Funct Materials - 2022 - Liu - Toward Excellence of Electrocatalyst Design by Emerging Descriptor‐Oriented Machine.pdf

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2110748 (1 of 25)
development, it is still far from meeting
the increased demand.[3]
The emergence of artificial intelligence
(AI) has fundamentally changed the situ-
ation, which has significantly accelerated
the discovery process, owing to greatly
improved algorithms and developments in
data science.[4]
Machine learning (ML), a
simple and practical AI framework based
on computer and statistical science, is
used to develop algorithms to learn from
historic data without being explicitly pro-
grammed to obtain specific results.[5] It
can investigate relationships that are hard
to clearly and definitely model mathemati-
cally, providing insights for new scientific
advancements related to highly complex
with many uncertain twisted together
factors.[6]
There are usually three factors that
govern the learning and prediction pro-
cess of an ML: algorithms, data/database,
and descriptors.[4] The algorithms involve
data extraction, data filing, and propaga-
tion from mathematical derivation.[5,7] The
data can be derived not only from experiments but also from
theoretical calculations.[4] A number of databases based on
experiments[8] and calculations[9] have already been established.
The descriptors depend to a large extent on the predicted mate-
rial or properties. Based on the algorithm, databases, and
descriptors, the ML applications have been successfully imple-
mented to support various energy materials with analysis tools
(e.g., Python-based SciKit-Learn[10] and TensorFlow[11]) in com-
bination with workflow management tools (e.g., ASE[12]
and
Atomate[13]
). However, the prediction accuracy depends highly
on the descriptors, as descriptors have a certain uniqueness
for various materials and properties as long as the algorithm is
selected correctly and the data set is complete.[14]
For catalysis,
the descriptors contain the essence from the physicochemical
nature. Based on effective descriptors, ML can uncover the
relationship bridging structure and its activity, selectivity, and
stability.[5,15]
Thus, suitable descriptors must be established to understand
the structure–activity relationship. Although many efforts have
been made to accelerate the rational design of homogeneous
catalysts,[7b,16]
heterogeneous catalysts,[7b,16b,17]
and electroca-
talysis,[3,18]
the development of ML-assisted real catalysts is still
in its infancy. Despite these considerable research efforts, the
lack of universal selection tactics for descriptors bridging the
gap between activity and structures impedes the application
Review
Toward Excellence of Electrocatalyst Design by Emerging
Descriptor-Oriented Machine Learning
Jianwen Liu, Wenzhi Luo, Lei Wang, Jiujun Zhang, Xian-Zhu Fu,* and Jing-Li Luo*
Machine learning (ML) is emerging as a powerful tool for identifying quanti-
tative structure–activity relationships to accelerate electrocatalyst design by
learning from historic data without explicit programming. The algorithms,
data/database, and descriptors are usually the decisive factors for ML and
the descriptors play a pivotal role for electrocatalysis as they contain the
essence of catalysis from the physicochemical nature. Despite the consider-
able research efforts regarding electrocatalyst design with ML, the lack of
universal selection tactics for descriptors bridging the gap between structures
and activity impedes its wider application. A timely summary of the appli-
cation of ML in electrocatalyst design helps to deepen the understanding
of the nature of descriptors and improve the application scope and design
efficiency. This review summarizes the geometrical, electronic, and activity
descriptors used as input for ML training and predicting to reveal the general
rules for their application in the design of electrocatalysts. In response to the
challenges of hydrogen evolution reaction, oxygen evolution reaction, oxygen
reduction reaction, CO2 reduction reaction, and nitrogen reduction reaction,
the ML application in these areas is tracked for the progress and prospective
changes. Additionally, the potential application of the automated design and
discovery are discussed for the other well-known electrocatalytic processes.
DOI: 10.1002/adfm.202110748
1. Introduction
With the increasing demand for energy owing to the rapid
development of global economies, the discovery of high-per-
formance, stable, and sustainable materials for energy appli-
cations has become imperative.[1] The conventional energy
materials discovery method is usually based on trial-and-error
processes and takes 15 to 25 years (or longer) to achieve desired
applications.[2]
Although modern technology has accelerated the
J. Liu, W. Luo, L. Wang, X.-Z. Fu, J.-L. Luo
Shenzhen Key Laboratory of Polymer Science and Technology
Guangdong Research Center for Interfacial Engineering of Functional
Materials
College of Materials Science and Engineering
Shenzhen University
Shenzhen 518060, China
E-mail: xz.fu@szu.edu.cn; jingli.luo@ualberta.ca
J. Zhang
Institute for Sustainable Energy
College of Sciences
Shanghai University
Shanghai 200444, China
The ORCID identification number(s) for the author(s) of this article
can be found under https://doi.org/10.1002/adfm.202110748.
Adv. Funct. Mater. 2022, 2110748

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potential of ML. Understanding the selection rules for descrip-
tors is a prerequisite to its beneficial utilization. Timely review
of the application of ML in electrocatalyst design helps deepen
the understanding of the nature of descriptors and improve the
application scope and design efficiency.
This review summarizes the ML application in the field of
electrocatalysis from the selection tactics of descriptors for elec-
trocatalyst design and the improvement that machine learning
can make for the challenges of hydrogen evolution reaction
(HER), oxygen evolution reaction (OER), oxygen reduction
reaction (ORR), CO2 reduction reaction (CO2RR), and nitrogen
reduction reaction (NRR) (see Figure 1). To shed light on the
descriptor selection tactics, the geometrical, electronic, and
activity descriptors for the quantitative representation of elec-
trocatalysis are discussed in detail. Additionally, the progress
of machine learning in meeting the challenges of HER, OER,
ORR, NRR, CO2RR, etc., is reviewed, including the challenges
of reducing the loading of precious metals, improving catalytic
activity, and breaking the linear relationship of adsorption inter-
mediates. Afterward, the limits, problems, delays, etc., of ML in
electrocatalysis research are discussed. Finally, the challenges
and perspectives in the ML application for electrocatalysts
design are given for the potential energy storage and conver-
sion application, such as, hydrogen, methanol, ethanol oxida-
tion reactions, as well as, sulphur oxidation reactions for Li-S
batteries.
2. Descriptors for Electrocatalysis
For ML applications, descriptors are the key for developing
models that can handle in-depth domain knowledge about
physical material properties.[19]
Such properties can adequately
characterize the underlying physics and unique structures of
matter. A good descriptor is simple, easy to obtain, and low
dimensional.[20] To better understand the functions of these
descriptors, extensive prior research has been completed
on descriptors. For instance, Hong et al. systematically eval-
uate the descriptors for OER.[21] Andersen et al. systematically
studied 31 descriptors based on their OER reactions.[22] Liu and
co-workers used 38 descriptors for OER performance prediction
based on perovskite oxides using the surface center environ-
ment feature model.[23] These studies showed the importance
of descriptors, but they were not universally applicable to elec-
trocatalysis. To highlight the most-used descriptors, we summa-
rized those used for HER, OER, ORR, CO2RR, and NRR from
the recently published research articles. (see Table 1) Tradition-
ally, three types of feature descriptors are used with ML applica-
tions: geometrical, electronic, and activity-based.
As the properties are derived from the geometrical structures
of systems, they are usually referred to as structural descrip-
tors, including atomic radius/covalent radius, atomic number
(i.e., mass number), group number, molar volume, lattice con-
stants, rotational angle, bond length, coordination number,
active sites, and surface properties (i.e., defects/microstructure/
facet). Research has shown that some of the geometrical
descriptors are good indicators for the reactivity. For instance,
the coordination number has been shown as an excellent indi-
cator for structural properties of a catalyst that influences cata-
lytic performance,[24] which can predict the optimal active sites
for Pt (111).[24b] Jiang et al. have also showed that an adjusted
coordination number can act as a general descriptor to bridge
the structure and reactivity for the oxygen sites over transition-
metal oxides.[25]
In addition, if the properties are derived from electron den-
sity,[26] they are referred to as electronic descriptors. These
descriptors are usually obtained from electronic structure cal-
culations, which are time-consuming owing to the first princi-
ples/ab initio calculations required. It involves d bands/orbitals,
band gaps, s electrons, charge/charge difference, valence elec-
trons, etc. For transition metals, the main reactivity lies on the
d-bands/orbitals, whose properties include center, filling, width,
skewness, kurtosis, and density of states at the Fermi level, and
they play an essential role in electrocatalysis.[22,23] Hence, these
properties have been extensively used as descriptors.[27] The sur-
face valence band photoemission spectra provided experimental
evidences that the d-band center is indeed an active descriptor
for CO2RR[28]
and NRR.[29]
However, the d-band center endures
inaccuracy for early transition metals and strongly correlated
metals.[30]
Alternatively, Shao–Horn and co-workers illustrated
that the ORR activity for oxide catalysts basically related to the
filling of σ* orbital (eg) and the degree of B-site transition metal
covalency.[31]
In addition, the O p-band center with respect to
the Fermi level was found an active descriptors for (Ln0.5Ba0.5)
CoO3-δ double perovskites.[32]
Valence electron is another impor-
tant descriptor for reactivity alongside d-band/orbital related
properties since it directly participates the formation and
breakage of chemical bond. Koper et al. found that the number
of the outer electrons is a good descriptors for the adsorption
processes on transition metals and their oxides as the trends
of adsorption energies were well correlated with the number of
outer electrons.[33]
Another type descriptor is used to describe the ability to
accept or lose electrons/protons/groups to show the activity,
Figure 1. Overview of the descriptor-oriented ML electrocatalyst design.

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that is, activity descriptors. They include adsorption energy,
electronegativity, electron affinity, ionization potential/energy,
pKa, and so on, as shown in Table 1. Adsorption energy shows
the adsorption ability of a group on the surface of electrocata-
lyst. It could be employed as a descriptor to predict properties
for electrocatalysis (e.g., onset potentials, turnover frequencies,
and product selectivity),[34]
although it is usually a target prop-
erty for the ML predictions.[35]
These predicted properties are
usually based on scaling relations between binding
energies
of reaction intermediates, Brønsted–Evans–Polanyi rela-
tions between reaction energies and kinetic barriers. Electron
affinity is the difference of system energies without an electron
and that of the anion, whereas the ionization potential is the
minimum energy needed for an electron to detach from the
molecule. For ML applications, these two descriptors are
usually used simultaneously.
In addition, other descriptors also exist and usually are
used for specific systems. For example, Yin and co-workers
Table 1. Descriptors and their applications in ML for electrocatalysis.
Descriptors HER OER ORR CO2RR NRR
Atomic radius/covalent radius [36] [37] [38] [39] [40]
Atomic number/mass number [36a,41] [37b,37c] [1,41a] [40]
Group number [38c] [40a]
Molar volume [42] [42]
Lattice constants [36a,43]
Rotational angle [44] [44]
Bond length/bond information [36a,43–45] [38d]
Coordination number [41a,41c] [46] [24b,46,47] [1,41a,46,48] [40b,46]
Active sites [41b]
Surface properties (defects/microstructure/facet) [49] [49,50]
d bands/orbitals:
1) Centre [36,43] [37c,51] [51] [52] [53]
2) Hibert transformation [26]
3) Electrons [36b,41b,54] [37b,42,51] [42,47b,51,54] [39]
4) Orbitals information [36a]
5) eg occupation [37c,54] [54]
6) Width [37c] [52]
7) Skewness [37c] [52]
8) Kurtosis [37c] [52]
9) Pseudopotential radius [42] [42]
Band gap [44] [44]
s electrons [42] [42]
Charge/charge difference [55]
Valence electrons [41c,56] [37a,46] [38d,46] [46,48a] [46]
Adsorption energy [41a] [47b,57] [1,39,41a] [53]
Formation enthalpy [54] [58] [54] [39]
Charge-transfer energy [59] [60]
Electronegativity [36,41a,41b,56,61] [37,42,46,51,54,61a] [38a,38c,38d,42,46,47b,51,54,61a] [1,39,41a,46,48,52] [40,46]
Electron affinity (EA) [41b] [37b] [38a,38d,47b] [39,48b] [40a]
Ionization potential (IP)/energy [36b,41b] [37b] [38c,38d,47b] [39,48b] [40a]
pKa [54] [54]
Chemical potential [62]
Strain [42] [42]
Tolerance factor [63]
Octahedral factor [63]
Fukui function [64]

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developed the octahedral (µ) and tolerance (t) factors ratio as
a simple descriptor to accelerate the discovery of new perov
skite catalysts with superior OER activity since the tolerance
and octahedral factor have well known geometrical/structural
interpretations for perovskite.[63] Gusarov et al. developed an
unused sort of ML-enhanced descriptor based on the Fukui
function, which provided information about the local
system’s response to perturbations and was used as a descriptor
to describe the chemical properties of surfaces.[64] In addition,
Shao-Horn et al. summarized that the electrochemical redox
potential can act as an efficient descriptor for OER and ORR.[65]
3. Machine Learning Application for
Electrocatalyst Design Based on Calculations
The application for the electrocatalyst design involves mainly
oxidation and reduction reactions. In this section, we review
these redox reactions for electrocatalysis applications based on
the calculations independent of experiments. These involve
HER, OER, ORR, CO2RR, and NRR.
3.1. Machine Learning for Hydrogen Evolution Reaction
Application
The production of hydrogen has attracted extensive attention
for providing pollution-free energy with high energy density.[66]
To this end, HER has emerged with paramount significance
for hydrogen energy conversion and storage. HER is a classic
two-electron transfer reaction with only one intermediate H*,
where * denotes the adsorption. This principle is applied in the
following sections. A two-electron transfer reaction might occur
via either the Volmer–Tafel or the Volmer–Heyrovsky mecha-
nism (see Table 2).
These mechanisms are the basis for ML-auxiliary electrocata-
lyst design, and have been reviewed by many researchers.[3,66,67]
Because all mechanisms involve the adsorption of hydrogen,
the optimization of the adsorption of hydrogen is the main
target for catalyst design according to the Sabatier principle.
Pt-based precious-metal electrocatalysts dominate HER appli-
cations,[68] although some non-precious metals composites are
effective.[67c] To make electrocatalysts more cost-effective, it is
desirable to minimize the dosage of Pt or to use non-precious
metals. The construction of precious-metal alloys and stable
non-precious-metal electrocatalysts are new strategies for ML
HER applications.
Table 2. Mechanisms for HER in acidic and alkaline environments.
Mechanisms Reactions
Volmer-Tafel Volmer step: H+
+ e−
+ * → H*
Tafel step: 2H* → H2 + *
Volmer-Heyrovsky Volmer step: H+
+ e−
+ * → H*
Heyrovsky step: H* + H+
+ e−
→ H2 + *
Figure 2. a) Illustration for the random sampling for a bimetallic alloy (100) surface. b) Algorithmic architecture of the BPNN model employed for the
random sampling of bimetallic alloys. Reproduced with permission.[36a] Copyright 2020, Royal Society of Chemistry.

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Based on conditional combinations of ML regression algo-
rithm, density functional theory (DFT) data and descriptors, Yang
and co-workers explored the catalytic activity of bimetallic alloy
(100) surfaces by alloying strong (Pd and Pt) and weak-binding
(Ag, Au, and Cu) transition metals as shown in Figure 2.[36a] They
varied the contents for MxNy (x + y = 1) for screening the high-
performance electrocatalyst, as shown in Figure 2a. The results
indicated that PdxAg1-x and PdxAu1-x possessed the foremost
promising HER activity in acidic environment, owing to the pro-
foundly active fourfold ensembles. Combining the geometrical,
electronic and activity descriptors such as the electronegativity,
d-orbital atomic radius, lattice constant, and atomic number as
descriptors (Figure 2b), additional ML analyses based on three-
layer artificial neural networks (ANNs) using a back-propagation
neural-network algorithm predicted that the Pt4/Ir0.75Pt0.25 (100)
would show the most active electrocatalyst for HER among the
≈900 predicted bimetallic alloy structures.
Ulissi and co-workers created a workflow from the ideas ini-
tiated by active ML- and surrogate-based optimizations, which
used a less-consuming surrogate model to supplant a com-
putational cost model to optimize the objective function.[41a]
With this method, a surrogate model was first created from a
given dataset and used to select data based on the geometrical
and activity descriptors, that is, atomic number, coordina-
tion number, electronegativity, and adsorption energy. Then,
the selected data were added to the original dataset to create
an updated one. The surrogate model continuously improved
through this repetition. Using this workflow, the researchers
screened 50% of the d-block elements and 33% of the p-block
elements, and 258 surfaces across 102 alloys were identified for
experimental validation. The distinguishing proof of surfaces
with near-optimal ΔEH values are shown in Figure 3. The work-
flow could be effortlessly transplanted to other reaction systems
if the perfect thermodynamic descriptors are known.
Figure 3. Near-optimal ΔEH values for HER. a) Identified near-optimal surfaces number and violin plots versus time. b) The normalized distribution
for the DFT calculated ΔEH of all the surfaces. c) Surface for DFT and ML calculated ΔEH at low coverage. d) Surface for ML calculated ΔEH only at low
coverage. Reproduced with permission.[41a] Copyright 2020, Springer Nature.

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Non-metal electrocatalysts are alternatives to Pt. Hence, a
recent ML HER application focused on 2D materials,[41b,41c,43,44,69]
especially those supporting single-atom catalysts (SACs) such
as, graphdiyne (GDY),[41b] metal–nitrogen–carbon (M–N–C),[36b]
MXene,[43] MoS2,[41c] and transition-metal dichalcogenide
(TMD).[44]
Huang and co-workers applied DFT calculations to ML to
systematically investigate the HER process catalyzed by GDY-
based atomic catalysts covering all transition and lanthanide
metals with adsorption energies, adsorption trends, electronic
structures, reaction pathways, and active sites.[41b] Six descrip-
tors were employed for the input data of ML, including two
geometrical descriptors (mass numbers and active sites), one
electronic descriptor (d/f electrons) and three activity descrip-
tors (electronegativity, electron affinity, and ionization poten-
tial). The ML bag-tree approach was employed based on data
separation and converse prediction fuzzy models to estimate
HER performance, followed by DFT calculations. The methods
gave nearly the same results, indicating their highly accurate
ML prediction capability.
Because recent studies have illustrated that 2D M–N–C-
based SACs exhibit superior performance for HER,[70] Jiang and
co-workers reported a new tuning approach by altering the size
and dimensionality of the M–N–C carbon substrate whereas
keeping up the same coordination environment.[36b] Taking the
geometrical descriptors (covalent and Zunger d-orbital radii),
electronic descriptors (d band center, d electrons number and
states) and activity descriptors (formation energy, electron-
egativity, ionization potential) into consideration, they set up
ML models and screened the 3–5d transition-metal SACs in
N-doped graphene and nano-graphene of several sizes of HER
using a DFT, predicting that nano-graphene involving V, Rh,
and Ir would have significantly improved HER activity. Wang
and co-workers used four ML models alongside DFT to accel-
erate the HER catalysts screening in 299 MXene materials.[43] It
was found that the random-forest algorithm gave high accuracy
based on simple elemental descriptors. Additionally, they evalu-
ated correlationship between the descriptors (d-band center,
Bader charge transfer, bond length, the lattice parameter) and
the adsorption energy ΔGH. It was found that simply descriptors
cannot establish a good relationship with ΔGH. The integration
of multiple descriptors is necessary for a more accurate predic-
tion of ML. In addition, Liu et al. also studied the electronic and
composition attribution to the catalytic activity based on TMDs
and established an equation for ΔGH prediction of TMDs based
on activity descriptor electronegativity and electronic descriptor
valence electrons.[56]
Moreover, Goddard III and co-workers pro-
posed that the optimization of descriptors would dramatically
improve catalytic performance.[44]
They applied the least abso-
lute shrinkage and selection operator process, incorporating
unconventional descriptors such as, rotational angle, layer dis-
tances, and bandgaps ratio of component materials with DFT to
predict novel structures. It predicted that MoTe2/WTe2 would be
high performance electrocatalyst with overpotentials of 0.03 V
and 0.17 for HER and OER, respectively.
Collectively, previous works on the design of alloy and simple
2D materials suggest that ML models are highly competitive
in accelerating electrocatalyst design for HER, exhibiting a
decent prediction precision. The geometrical, electronic, and
activity-based descriptors are deeply involved for the ML HER
applications. The combination of these descriptors is critical for
the efficient ML applications. The integration of geometrical
and electronic descriptors with activity descriptors improves
the ML prediction significantly. Notably, the electronegativity is
found essential for the ML prediction of electrocatalytic activity
as electronegativity is the ability of an atom to attract an elec-
tron pair shared with another atom to form a chemical bond,
which is an indicator of intrinsic activity properties. To this end,
the selection tactics of descriptors is one of the most critical fac-
tors for the electrocatalyst design based on ML.
3.2. Machine Learning for Oxygen Evolution Reaction
Application
OER is another half reaction for water splitting. However, owing
to the sluggish kinetics of four-electron transfer reactions, OER
is a bottleneck for electricity-driven water splitting.[71] Hence,
the mechanisms for OER in acidic and alkaline environment
have been intensively reviewed as shown in Table 3,[72] indi-
cating that one of the main challenge for electrocatalyst design
lies with the strong correlations of adsorption energies for
intermediates *OH, *O, and *OOH.[73] Breaking the correlation
relationship to achieve superior performance is the main goal
of OER electrocatalyst design. Recently, ML-based electrocata-
lyst design has focused on modelling the precious-metal oxide
IrO2,[50,58,74] and non-precious metal oxides, for example, spinel
oxides,[37a] perovskite,[23,37c,63] quaternary metal oxides,[75] and
2D materials.[37b,44,51,54] In this section, some examples are given
for these materials application.
3.2.1. Iridium Oxide
Nowadays, iridium oxide is one of state-of-the-art electrocata-
lysts for OER and IrO2 is usually used as the benchmark for
OER.[72a] Understanding the oxygen chemistry of these mate-
rials is essential for OER performance enhancement, but it is
complicated. Bligaard and co-workers studied the relationship
between IrO2/IrO3 polymorphs and OER functionality by cou-
pling active ML with subsequent analysis.[58] For each IrO2/
IrO3 polymorph, surfaces were established by cutting along the
Miller indices with the sharpest diffraction peaks associated
Table 3. Reaction mechanisms for OER in acidic and alkaline
environments.
Environment Mechanisms
Acidic * + H2O (l) → *OH + H+ + e−
*OH → *O + H+
+ e−
*O + H2O (l) → *OOH + H+
+ e−
*OOH → * + O2 + H+
+ e−
Alkaline * + OH−
→ *OH + e−
*OH + OH− → *O + H2O + e−
*O + OH− → *OOH + e−
*OOH + OH−
→ * + O2 (g) + H2O(l) + e−

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with planes having higher atom density (see Figure 4a). The
results showed that surface stability analysis was pivotal for pre-
cise determination of the activity, revealing that octahedral coor-
dination were preferable for about all low-energy structures.
Additionally, Pourbaix Ir-H2O investigation showed that α-IrO3
was remarkably stable under acidic environment superior to
that of IrO2. The activity plot for the OER against the descriptor,
ΔGO−ΔGOH (Figure 4b), displayed two thermodynamic limiting
potential volcanos and kinetic OER volcanos, which was in good
agreement with the strong binding portion and exhibit a sim-
ilar optimum value. The α-IrO3 (100), (110), and (211) showed
the highest performance for the surface structures with high
oxygen coverage (Figure 4c). Around 0.4 VRHE overpotentials
have been observed for these surfaces, outperforming R-IrO2
with ≈0.2 VRHE improvement. It confirmed the onset poten-
tials shift experimentally. The main reason for the OER activity
enhancement was the higher oxidation state (Ir6+
) of IrO3 with
three 5d-electrons compared with the low oxidation state (Ir4+
)
of IrO2 with five 5d-electrons. Therefore, oxygen-saturated IrO3
bound OER intermediates more weakly, leading to positive
shifts of ΔGO–ΔGOH. To this end, the descriptor ΔGO–ΔGOH
involved in this work can be regarded as a composite descriptor,
including both activity descriptor (adsorption energy) and elec-
tronic descriptors (d electrons) to account for the electrocata-
lytic activity. With this approach, 956 different type AB2 and AB
structures were identified among 38 000 existing materials in
the databases. 196 IrO2 polymorphs were found thermodynami-
cally stable, and 75 IrO3 polymorphs were found synthesizable.
Finally, α-IrO3 was reported as the most stable.
Ulissi et al. proposed an automated method to help under-
stand oxygen chemistry while predicting OER overpotentials
for universal oxide surfaces making use of the descriptor ΔGO-
ΔGOH in combination of surface information.[50]
It was found
that low-index surfaces of IrO2 were more active and the IrO2
Figure 4. a) Pourbaix diagrams for R-IrO2, α-IrO3, R-IrO3, and β-IrO3. b) OER activity volcano for IrOx using ΔGO−ΔGOH as the descriptor. c) Models
for selected OER surfaces with monolayer O* coverage. Reproduced with permission.[58]
Copyright 2020, American Chemical Society.

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and IrO3 were identified with the most promising active sites,
which were superior to rutile (110) by 0.2 V in theoretical over-
potential. Moreover, they provided catalyst design strategies for
improving the activity of Ir-based catalysts and an ML model
that could predict surface coverages and site activity based on
the DFT calculations. Reuter and co-workers also identified
IrO2 surface complexions through ML.[74] Using simulated
annealing, they trained a Gaussian approximation potential
using DFT data to construct a global geometry optimization for
low-index rutile IrO2 facets. The (101) and (111) (1 × 1) termina-
tions were surprisingly identified by ab initio thermodynamics
that compete with (110) in reducing environments, which
was confirmed by single-crystal analysis experimentally. The
unexpected surface structures identified for such well-studied
system indicates the powerful predictive quality of ML.
3.2.2. 2D Materials
2D van der Waals heterostructure materials were proven to
be excellent water-splitting electrocatalysts to produce H2 and
O2.[66] Therefore, these kinds of materials have received con-
siderable attention. The usage of 2D materials for OER could
significantly lower precious-metal loading while facilitating
the activity. Based on DFT calculations of graphene-supported
SACs, Chen and co-workers built ML models to portray the
latent pattern of easily available physical properties and limiting
potentials, employing these models to forecast the electrocata-
lytic performance of other graphene-supported SACs involving
metal-NxCy active sites. Integrating the electronic descriptor
(d electron number) and activity descriptors(oxide formation
enthalpy, electronegativity, and pKa), they recomputed the best
catalysts prescribed by the ML demonstrate toward the OER
using DFT, confirming the high reliability of their ML dem-
onstrations. Further, the Ir incorporated graphene with 2 and
3 pyridine-N atoms dopant OER catalyst (Ir-N3-C1 and Ir-N2-C2)
were identified to outperform RuO2 and IrO2.[54] Li and co-
workers used atomic mass, atomic radius, d-electron, electron-
egativity, electron affinity, and ionization energy as descriptors
to predict the overpotential for OER of single-atom catalysis.[37b]
Owing to the maximum atomic efficiency,[76] it can predict the
overpotential precisely and quickly for OER catalyzed by SACs
and found the prediction was similar to these from DFT calcu-
lations but 130 000-fold reduction of time.[37b]
3.2.3. Spinel Oxide
Along with IrO2 enhancements, the usage of other oxides as
alternatives has been notable, among which spinel oxide (AB2O4)
is representative. Xu and co-workers showed that the activity of
AB2O4 toward OER was inherently overwhelmed by the competi-
tion between tetrahedral (A2+
cation), and octahedral (B3+
cation)
covalency (see Figure 5).[37a]
Owing to the crystal field effect,
the d-orbitals of the tetrahedral cations were split into three t2-
orbitals and two e-orbitals whereas the d-orbitals of octahedral
cations were split into three t2g-orbitals and two eg-orbitals due
to symmetry difference. These types of bonds formed MTO
and MOO due to the orbital overlapping between the metal
d-orbitals and oxygen p-orbitals. Because the tetrahedral (A2+
cation) and octahedral (B3+
cation) cations were alternately con-
nected, each oxygen atom was shared by these cations via the
p-orbitals overlapping, leading to covalency competition. This
subsequently resulted in non-equivalent bonds for MTO and
MOO with one stronger, forming asymmetrical backbone with
structure of MT−O−MO. In the case of bias applied for the OER
application, the surface reconstruction of spinel oxides might
happen, and weaker bond might break. Once the weaker bond
broke, the MTOMO was separated into two parts, MO and
M. The coordination of the cations in MO remained full, so
it hardly contributed to the performance enhancement. However,
the coordination of the bared M was changed with unpaired
valence electrons, which could serve as active sites to start OER
cycles. To this end, the breakage of either MTO or MOO
from MTOMO could generate exposed cation sites to acti-
vate the OER cycles. Thus, the weaker metal–oxygen covalency
of MTOMO backbone determined the exposure of cation sites
and therefore its activity. Driven by this discovery, more than
300 spinel oxides were calculated to train an ML model to screen
spinel oxides, and [Mn]T[Al0.5Mn1.5]OO4 was forecast to be a
highly active OER catalyst, which was confirmed experimentally.
3.2.4. Perovskite
Perovskite is another active non-precious metal oxide electrocat-
alyst for OER. It possesses a regular ABO3-type structure. The
ABO3-type structure is flexible with various component options
for A and B, which leads to a combinatorically large number
that can be estimated based on combinatorics.[31a] Xin and co-
workers developed an adaptive ML method to search ABO3-
type perovskites for high-performance OER activity with a set of
multi-fidelity features and probabilistic models.[37c] The set fea-
tures included composition and electronic structures, whereas
the probabilistic models were trained by Gaussian processes with
ab initio calculation data for predicting *O and *OH adsorption
energies and other activity descriptors. A univariate analysis of the
discrepancy of probability density functions (pdf) was performed
to discover the physical factors determining the OER activity
using the Kullback–Leibler (KL) divergence, an indicator to dis-
tinguish informative descriptors. Figure 6a,b show that small KL
divergence values were obtained for the descriptors A-site elec-
tronegativity and tolerance factor as the pdf distributions were
mostly overlapped for the OER activity samples. In comparison,
the high divergence values were obtained for the descriptors
dx y
2 2
− orbital center and dz2 orbital filling due to the mismatched
pdf distribution (Figure 6c,d). Additionally, Figure 6e highlighted
all highest constructive descriptors, showing that the eg orbitals
dz2 , dx y
2 2
− fillings with high KL divergence values were strongly
correlated to the OER activity of perovskite. The further univar-
iate analysis demonstrated that the electronic descriptors as phys-
ically instinctive highlights were invaluable to understanding the
fundamental physical laws that determine the OER activity at the
molecular level. The finds agreed with the experimental observa-
tion that the occupancy of eg orbital for the metal B site mainly
determines the OER activity. The main reason lay in the fact that
the eg orbital dz2 interacted with the p-orbitals of oxygen interme-
diates with overlapping at active sites. By evaluating the potential

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perovskites with theoretical overpotentials <0.5 V, the ML models
rapidly screened ≈4000 double perovskites and selected the stable
structures with potential high-performance OER activity.
Yin and co-workers used symbolic regression to design
new oxide perovskite electrocatalysts with improved OER
activities.[63]
As the larger tolerance and octahedra factor lead
to structure distortions and instability of oxygen in the perov-
skite, the ratio of octahedral and tolerance factors (µ/t) was
used to accelerate the discovery of a number of new perovskite
electrocatalysts having improved OER activity. Based on the
descriptor, a few new perovskites having potentially high OER
activity were synthesized, among which four new ones (i.e.,
Cs0.4La0.6Mn0.25Co0.75O3, Cs0.3La0.7NiO3, SrNi0.75Co0.25O3, and
Sr0.25Ba0.75NiO3) showed excellent intrinsic OER activities.
3.2.5. Quaternary Metal Oxide
Gregoire et al. accelerated the material discovery process
using updating ML sequential learning (SL) based on the
quaternary metal-oxide electrocatalysts designed for OER to
quantify superior electrocatalyst performance and accuracy.[75]
The overpotential of OER was chosen for the performance
metric. Various SL schemes were examined on four chemical
components, each containing 2121 catalysts (see Figure 7).
Their work suggested that electrocatalyst design could be
accelerated by up to a factor of 20 compared with random
acquisition methods (RCM) in particular scenarios. Further,
they showed that certain choices of SL models were not suit-
able for a given investigative goal, resulting in a significant
slowdown compared to RCM.
The evidence presented in this section suggests that the
ML application of OER was designed for the precious metal
oxide, IrO2, to better understand the reactivity origin and
optimize performance. ML applications for 2D materials
aim to lower the loading of precious metals. Moreover, non-
precious metals, such as, spinel oxides, perovskites, and
quaternary metal oxides, were used to design ML for new
OER electrocatalysts to replace precious metals. The value
of ΔGO−ΔGOH can act as excellent descriptor to exhibit the
Figure 5. OER mechanisms for spinel oxides based on the density of states and the machine learning prediction results. Reproduced with permission.[37a]
Copyright 2020, Springer Nature.

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correlation of *OH and *O and construct the vancono curve
to locate the optimal electrocatalyst. Although some pro-
gress was made in improving the reaction activity, reducing
the loading of precious metals, and designing non-precious
metal catalysts, the correlation between the *OH, *O, and
*OOH remains one of the main obstacles to OER electro-
catalyst design.
3.3. Machine Learning for Oxygen Reduction Reaction
Application
For the energy conversion process in energy storage and con-
version equipment such as fuel cells, the ORR plays a pivotal
role in the electrocatalytic process.[77] The slow kinetics of the
cathode limits the overall performance of fuel cells.[77] Hence,
Figure 7. a) Illustration for compositions containing 1–4 cation elements. b) The 2121 OER overpotentials for the 6, 15, 20, and 15 compositions con-
taining 1–4 cations. Reproduced with permission.[75] Copyright 2020, Royal Society of Chemistry.
Figure 6. Probability distribution plots for perovskite a) A-site electronegativity as descriptor, b) tolerance factor as descriptor, c) B-ion 2 2
dx y
−
orbital
center as descriptor, d) B-ion 2
dz orbital filling as descriptor. e) Polar distribution plots for the most informatic descriptors with KL entropy index > 0.4.
Reproduced with permission.[37c] Copyright 2020, American Chemical Society.

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accelerated electrocatalyst design is highly desired to facilitate
the kinetics of ORR for the fuel cells. Nowadays, the expensive
and efficient Pt-based materials are the most practical electro-
catalyst for ORR. However, the high price for electrocatalysts
becomes a challenge for the large-scale commercialization of
fuel cells. Thus, ML applications to find efficient ORR catalysts
are of paramount importance.
In acidic and alkaline environments, the mechanisms of
ORR have been well-reviewed,[77,78] as shown in Table 4. The
greatest challenge to ORR catalyst design lies with the unfa-
vorable scaling relationships between the binding energies of
reaction intermediates, *OH, *O, and *OOH for OER.[77] ML
has been used to discover efficient ORR catalysts to minimize
the loading of precious metals and to improve the design of
non-precious-metal electrocatalysts (e.g., 2D materials,[38a,47b,54]
high-entropy alloys (HEAs),[38b] and precious-metal core–shell
nanostructures).[47a]
3.3.1. 2D Materials
2D materials are attractive non-precious electrocatalysts because
Fe-N-C- and Co-N-C-based electrocatalysts have been found to
be active for ORR.[79] However, the Fenton effect for Fe-N-C, its
low activity and its low stability remain obstacles to their wide
application.[79] Therefore the design of stable, active 2D mate-
rial electrocatalysts for ORR-based MLs is extremely prom-
ising. Bi-atom catalysts might provide solutions by constructing
the synergy effect, as shown in Figure 8. Li and co-workers
unveiled design principles of 2D graphene-based dual-metal-
site catalysts for ORR using DFT with ML.[38a] This ML study
revealed that the ORR activity of dual-metal-site catalysts was
intrinsically determined by activity descriptors (electron affinity
and electronegativity) and the geometrical descriptor (radii of
embedded metal atoms). Huang et al. illustrated that 31 SACs
had the potential to break the scaling relations of *OH, *O,
and *OOH from 210 2D SACs by manipulating the supporting
environment of the materials. Eight descriptors were involved,
including geometrical descriptors (coordination number),
Table 4. Reactions mechanisms for ORR in acidic and alkaline
environment.
Environment Electrons transferred Reactions
Acidic 4 O2 + 4 H+
+ 4e−
→ 2 H2O
2 O2 + 2 H+
+ 2e−
→ H2O2
H2O2 + 2 H+ + 2e−→2 H2O
Alkaline 4 O2 + 2 H2O + 4e− → 4OH−
2 O2 + H2O + 2e−
→ HO2
−
+ OH−
H2O + HO2
−
+ 2e−
→ 3OH−
Figure 8. a) Illustration of the structures for dual-metal-site catalysts; b) ORR activity trends plot of dual-metal-site catalysts versus both ΔGOOH* and
ΔGOH*; c) free energy diagrams of dual-metal-site catalysts; d) simulated ORR polarization curves for 8 screened dual-metal-site catalysts versus Pt
(111). Reproduced with permission.[38a] Copyright 2020, American Chemical Society.

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electronic descriptors (d/p electrons), and activity descrip-
tors (the oxide formation enthalpy, electronegativity, elec-
tron affinity, and first ionization energy) and some composite
descriptors such as sum of the electronegativity of neighboring
C and N atoms. The predicted electrocatalysts simultaneously
achieved high activity and selectivity toward H2O2 production,
among which seven SACs were equipped with higher activity
than PtHg4 in acidic media.[47b] Notably, multiple-variable anal-
ysis discovered that the underlying origin of the selectivity and
activity arising from the charge transfer between the active site
and OOH* intermediate, as well as, the MO band hybridi-
zation, which provides hints for the electrocatalyst design to
enhance the activity and selectively simultaneously.
3.3.2. High-Entropy Alloys
HEAs comprised various elements in solid solutions to form
well-ordered crystal structures with randomly distributed con-
stituents, offering atomic arrangement sites having extraordi-
nary catalytic properties. With such manipulations, the loading
of the precious metal could be lowered while enhancing its per-
formance. Rossmeisl and co-workers made HEA the discovery
platform for ORR based on the activity descriptor (adsorption
energy) and geometrical descriptors (composition of the local
binding site).[38b] Making use of DFT in combination of ML,
they found that the calculated and predicted values of *OH and
*O adsorption energies were in good agreement on any subset
of available binding sites. With a complete list of available
adsorption energies, this excellent expression of electrocatalytic
activity prediction was employed to optimize the composition
of HEA. As a result, the HEA was changed to a design platform
for unbiased discovery of new alloys by optimizing sites with
special electrocatalytic activity. Specifically, the results predicted
that the binary alloy IrPt significantly enhanced the perfor-
mance compared to pure Pt (111).
3.3.3. Core–Shell Nanostructure
Construction of core–shell nanostructures is another effec-
tive strategy of lowering the loading of precious metals.
Gagliardi and co-workers presented an ML framework that
introduced strain to enhance ORR activity for Pt core–shell
nano-catalysts.[47a]
Based on the geometrical descriptor general-
ized coordination number, they demonstrated that the optimal
strain depended on the nanoparticle size or the weakening of
the compressive strain. It was predicted that bimetallic Pt@Au
and Pt@Ag would have the best mass activities at 2.8 nm, as
long as active sites were exposed to weak compressive strain.
This work is mainly for precious metals, which have been
proven to have good activity for ORR. So the generalized coordi-
nation number can be used as the descriptor solely to optimize
the geometric structure. However, the usage for non-precious
metals to design efficient electrocatalyst has not been reported
yet, which requires further exploration.
Overall, there were two general strategies for electrocatalyst
design based on ML application to ORR: Searching for alter-
native electrocatalysts (non-precious metal electrocatalysts) or
lowering the loading of precious metals. The adsorption ener-
gies of *OH, *O, and *OOH are essential activity descriptors
for the electrocatalyst design. Note that it could break the corre-
lation between *OH, *O, and *OOH to produce H2O2 with both
high selectivity and activity by manipulating the supporting
environment of the 2D materials. Although some descriptors
such as coordination number can be solely used as indicator for
ML application, the combination of geometric, electronic, and
activity descriptors is usually an efficient strategy for the predic-
tion of ORR electrocatalysts.
3.4. Machine Learning for CO2 Reduction Reaction Application
Electrochemical CO2 reduction to value-added chemicals and
fuels has attracted extensive attention because it provides a
clean and effective method to alleviate energy shortages while
reducing global carbon emissions.[80] Electrochemical reduction
methods of CO2 are varied, producing 16 different products,
including C1 products (i.e., CO, HCOOH (formic acid), HCHO
(formaldehyde), CH3OH (methanol), CH4 (methane)) and
multi-carbon products (i.e., H2C2O4 (oxalic acid), CH3CH2OH
(ethanol), CH2 = CH2 (ethylene), CH3CH3 (ethane), and
CH3CH2CH2OH (n-propanol)), which have been well-sum-
marized in previous research.[81] The 2–18 electron reduction
reactions are shown in Table 5. Owing to the diversity of prod-
ucts, selectivity has become one of the most concerning issues
for electrocatalytic CO2RR.[80,81,81e] To account for this, the ML
application of electro-catalyzed CO2RR was performed[82] while
Table 5. Reactions, potentials (E0
vs SHE and pH = 7) and electron
transferred (n) for the CO2RR.
n Reactions[81a] E0
2 CO2 + 2H+ + 2e− → HCOOH −0.610 V
CO2 + 2H2O+ 2e− → HCOOH + 2OH− −1.491 V
CO2 + 2H+ + 2e− → CO + H2O −0.530 V
CO2 + H2O+ 2e− → CO + 2OH− −1.347 V
2CO2 + 2H+ + 2e− → H2C2O4 −0.913 V
2CO2 + 2e−
→ C2O4
2−
−1.003 V
4 CO2 + 4H+
+ 4e−
→ HCHO + H2O −0.480 V
CO2 + 3H2O+ 4e−
→ HCHO+4OH−
−1.311 V
CO2 + 4H+ + 4e− → C + 2H2O −0.200 V
CO2 + 2H2O + 4e−
→ C + 4OH−
−1.040 V
6 CO2 + 6H+
+ 6e−
→ CH3OH + H2O −0.380 V
CO2 + 5H2O+ 6e−
→ CH3OH + 6OH−
−1.225 V
8 CO2 + 8H+ + 8e− → CH4 + 2H2O −0.240 V
CO2 + 6H2O + 8e−
→ CH4 + 8OH−
−1.072 V
12 2CO2 + 12H+
+ 12e−
→ CH2 = CH2 + 4H2O −0.349 V
2CO2 + 8H2O + 12e− → CH2 = CH2 + 12OH− −1.117 V
2CO2 + 12H+
+ 12e−
→ CH3CH2OH + 3H2O −0.329 V
2CO2 + 9H2O + 12e− → CH3CH2OH + 12OH− −1.157 V
14 2CO2 + 14H+ + 14e− →CH3CH3 + 4H2O −0.270 V
3CO2 + 18H+ + 18e− → CH3CH2CH2OH + 5H2O −0.310 V

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focusing on alloys,[1,41a,48a,52,64,83] 2D materials,[39,62] and a data-
driven framework.[84]
Copper is unique to CO2RR because it adsorbs CO strongly
to inhibit the production of CO and formic acid. However, it
interacts with H weakly to suppress the formation of H2.[83a]
Hence, Cu is the predominant metal electrocatalyst for CO2RR
since the adsorption CO is regarded as an ideal descriptor
for catalytic performances of CO2RR.[85]
However, the energy
efficiency and productivity achieved cannot meet the criteria
for producing ethylene at cost-competitive prices. Hence, the
construction of alloys has become predominant. Ulissi and
co-workers presented a fully automated screening strategy
that used a combination of ML and DFT calculations to pre-
dict electrocatalyst performance of CO2RR with the same geo-
metrical and activity descriptors as mentioned above.[1,41a]
As
Figure 9a,b shows, the Cu-Al alloy was found to be the most
promising electrocatalyst for the reduction of CO2 to ethylene
with a very high Faradaic efficiency of over 80% amongst 244
various Cu-containing alloys by screening 12 229 surfaces
and 228 969 adsorption sites. The Cu-Al alloy also exhibited
the most adsorption sites with near-optimal CO adsorption
values, indicating a large range of adsorption feasibility for
surface compositions and adsorption sites (Figure 9c). The t-
SNE diagram in Figure 9d reveals that the binding for Al sites
was weak, whereas, the bonding of Cu sites surrounded by Al
atoms was strong for CO. As a result, the bridge sites of Cu-Al
surrounded by Cu atoms were active. In situ X-ray absorption
measurements have confirmed that CC dimerization can be
mainly attributed to the favorable Cu coordination environment
arising from Cu and Al alloys.
Rossmeisl and co-workers presented a discovery approach
of selective and active catalysts for the CO2RR on more com-
plicated HEAs.[83a]
By combining DFT with a supervised ML,
they predicted CO and H adsorption energies of the (111) sur-
faces for disordered CoCuGaNiZn and AgAuCuPdPt HEAs,
providing an optimal strategy for suppressing H2 formation by
weakening H2 adsorption and facilitating the reduction of CO
by strengthening its adsorption. The approach led to the unbi-
ased discovery of electrocatalyst having high selectivity.
Additionally, gold nanoparticles and de-alloyed Au3Fe
core–shell nanoparticles surfaces also showed enhanced per-
formance for the formation of CO from CO2RR. Goddard III
Figure 9. a) Activity volcano for CO2RR by the ΔECO versus ΔEH. b) Selectivity volcano for CO2RR by the ΔECO versus ΔEH. c) t-SNE representation of
adsorption sites for Cu-containing alloys based on DFT calculations. d) Representative coordination sites. Reproduced with permission.[1]
Copyright
2020, Spring Nature.

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and co-workers combined ML, multiscale simulations, and
quantum mechanics to predict the performance of surface sites
on gold nanoparticles and de-alloyed Au surfaces, identifying
the optimal active sites for CO2RR with far fewer calculations
than normal.[83b] A methodology based on α-value-mapping was
developed to discover the catalytic activity of an entire surface,
and two neural network based on ML models were developed
to accurately predict CO adsorption energy and hydro-car-
boxyl formation energy on extremely distorted and disordered
Au surfaces (see Figure 10). Applications of these models to
Au nanoparticles and de-alloyed Au surfaces resulted in the
identification of active sites and their features responsible for
enhancing CO2RR performance for disordered and irregular
surfaces. This strategy provided a powerful tool for discov-
ering the catalytic activity of an entire surface by comparing the
α-value with descriptors from experiments, computations, and
theory.
Because alloying is an effective method for enhancing the
efficiency of CO2RR, a good methodology for the universal
design is essential. To address the universal catalyst design
principle and illustrate structure–activity relationship of alloy
catalysts, Jiang et al. combined ML and descriptors (e.g., coor-
dination number, valence-electron number, electronegativity,
etc.) based on the inherent characteristics of the substrate, as
well as, adsorbents, and developed a model that allowed rapid
and large scale screening for alloys with accuracy similar to that
from DFT calculations.[48a]
The ML scheme shed light on active
center size, the alloying impact, and the coupling mechanism. It
not only helped with the understanding of the structure–activity
relationship of alloy catalysts and the reaction mechanisms of
CO2RR, but also provided a basis for catalyst design. Moreover,
Xin and co-workers presented an ML-enhanced chemisorption
model, which quickly and precisely forecast the surface reac-
tivity for metal alloys within a wide chemical space.[52]
They
showed that the trained ANNs based on electronic fingerprint
of idealized bimetallic surfaces and adsorption energies could
discover the complex nonlinear interaction relationship of the
adsorbate on multi-metallics with small error. Making use of
the proportional relationship between the adsorption ener-
gies of similar adsorbates, they illustrated that this integrated
approach significantly facilitated high-throughput catalyst
screening, while suggesting promising (100)-terminated multi-
metallic alloys with efficiency and selectivity enhancement for
CO2RR and C2 species.
In view of the discussion thus far, the ML application for
CO2RR mainly focused on improving selectivity and activity.
Cu-Al alloys were designed, and the design principles have
been examined in-depth for the formation of CO from CO2RR.
Due to the diversity of CO2RR products, the design of electro-
catalysts has also become particularly complicated. The design
of electrocatalysts for more value-added reactions required a
deeper understanding of the reaction.
3.5. Machine Learning for the Nitrogen Reduction
Reaction Application
Ammonia is a key chemical in fertilizers. However, the indus-
trially used Haber-Bosch process for NH3 production from N2
reduction is an energy-intensive chemical process that is highly
dependent on non-renewable fossil fuels.[86] It is increasingly
attractive to use renewable energy to reduce N2 to NH3 electr
ochemically.[86b,87] A major challenge for electrochemical NRR
is its low catalytic activity, selectivity and Faradaic efficiency.[88]
The main mechanisms have been addressed to understand the
nature using reversible hydrogen electrodes, standard hydrogen
electrodes, and normal hydrogen electrodes, as shown in
Table 6.[86a]
Currently, the ML application for NRR mainly
Figure 10. Active sites identification for AuNPs surfaces based on the α-values for all 11 537 surface sites. Reproduced with permission.[83b]
Copyright
2019, American Chemical Society.
Table 6. Reactions and potentials (vs RHE) for NRR.
Transferred
electrons
Reactions E0
1 N2 + H+ + e− → N2H −3.20 V (vs RHE)
2 N2 + 2 H+
+ 2e−
→ N2H2 −1.10 V (vs RHE)
4 N2 + 4 H+ + 4e− → N2H4 −0.36 V (vs RHE)
6 N2 + 6 H+ + 6e− → 3 NH3 (g) 0.55 V (vs NHE)
N2 + 6 H2O + 6e− → 2 NH3 + 6OH− (g) −0.736 V (vs SHE at pH = 14)

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focuses on the boron (B)-doped graphene single-atom[40b]
and
L12 crystal[40a]
catalysis.
Kim and co-workers used a ANN to design efficient electro-
catalysts for the NRR using boron-doped graphene SACs, which
could significantly reduce computation time by removing non-
efficient catalysts from screening.[40b]
As shown in Figure 11,
based on the ANN architecture with 10 neurons for each hidden
layer, the adsorption and free energies of intermediates repre-
senting the geometrical structure and bonding characteristics
can be predicted using the feature-based light-gradient boosting
machine model. Among the evaluated catalysts, CrB3C1 was
predicted as the most efficient electrocatalyst for NRR with a
minimal overpotential of 0.13 V. Further research revealed
that the average d-orbital occupation (around 4–6) is essential,
which could lower the limiting potential in addition to potential
overcoming the scaling relationship of the NRR.
To achieve acceleration electrocatalyst design of NRR, Kim
et al. developed a slab-graph convolutional neural network
(SGCNN) that accurately and flexibly probed surface catalysis
reactions (Figure 12).[40a]
For such SGCNN, only the elemental
properties and connectivity information were required as input,
which made the acceleration facile realization. Based on the
DFT-calculated and self-accumulated database, SGCNN pre-
dicted the binding energies for five key adsorbates for NRR,
Figure 11. a) ANN architecture with 10 neurons for each hidden layer. b) Feature–feature correlation map. c) DFT-calculations versus machine-learning
prediction. Reproduced with permission.[40b] Copyright 2020, Royal Society of Chemistry.

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that is, H, N2, N2H, NH, and NH2. The mean absolute error
was only 0.23 eV, indicating high accuracy for the predictions.
Four novel catalysts, that is, V3Ir, Tc3Hf, V3Ni, and Tc3Ta, were
found as potential electrocatalyst for NRR with both lower lim-
iting potentials and higher Faradaic efficiencies.
Collectively, ML applications for NRR could be used to meet
the challenge of low catalytic activity, selectivity, and Faradaic
efficiencies. Since the NRR is a complex multi-step reaction,
the activity and selectivity of its electrocatalyst still has a lot
of room for improvement. Similar to above mentioned elec-
trocatalysis, the combination of the geometric, electronic, and
activity-related descriptors is an efficient way for the ML appli-
cation for NRR.
Based on the summarization of the HER, OER, ORR,
CO2RR, and NRR, it is found that a unified selection method
has not yet been achieved due to the diversity of electrocatalytic
materials. Generally, atomic radius, atomic number, coordina-
tion number etc. geometrical descriptors, d-band center and
related properties, valence electrons, etc., electronic descrip-
tors, adsorption energy, electronegativity, electron affinity,
ionization energy, etc., activity descriptors are more common
used descriptors to date. Since a single descriptor is unable to
describe the entire electrocatalytic properties, these descriptors
are usually combined each other with comprehensive applica-
tions to achieve the excellence for the electrocatalyst design.
4. Machine Learning Application for
Electrocatalyst Design Based on Experiments
Due to the huge amount of calculations required, it is a big
challenge to predict the molecular/crystal structure based on
first principles/ab initio calculations.[89]
It is even more chal-
lenging to predict the products of a reaction based on the reac-
tants, because it requires a comprehensive understanding of
the potential energy surface of the reaction.[90]
Alternatively, A
ML approach based on the experiments can accelerate the pro-
cess.[91]
Usually, chemists typically design experiments based on
their intuition by understanding the structures/properties of
the reactants, patterns of reagent properties and composition
Figure 12. a) Elements used. b) The ordered intermetallic and core–shell binary catalyst systems. c) Illustration for key adsorbates in the NRR (upper)
and binding energy populations (ΔEads) for each adsorbate (lower). Reproduced with permission.[40a]
Copyright 2020, American Chemical Society.

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ratios that determine the synthesis. These intuitions imply the
information of the structure and properties of the reactants and
the relationship between them. The underlying relationship
can be mapped out by data-mining techniques from successful
and failed experiments, which can be subsequently used to pre-
dict the molecular/crystal structures and reaction products.
Based on failed experiments, Norquist et al. used ML trained
reaction data to predict reaction outcomes for the crystalliza-
tion of templated vanadium selenites.[92] In order to guide
future experimental design, they built a web-based database on
their own to record both successful and failed experiments in
details. The properties of the molecules (e.g., molecular weight,
number of hydrogen-bond donors/acceptors as a function of
pH and polar surface area), tabulated values of atomic prop-
erties (ionization potential, electron affinity, electronegativity,
hardness, and atomic radius), experimental reaction conditions
(for example, temperature, reaction duration, and pH), and
mole ratios of the different reactants, etc., were systematically
recorded. A support vector machine (SVM) model was then
built using those information of reactant properties. Based on
the test-set data, the single SVM model found that the predic-
tion accuracy is 78% for describing all of the reaction types, and
79% for vanadium-selenite reactions. Moreover, their ML model
outperformed traditional human strategies, and successfully
predicted conditions for new inorganic products with 89% suc-
cess rate for hydrothermal synthesis experiments.
The flow-chart representation for the SVM model is shown
in Figure 13. For the production of amines with moderate polar-
izability (shaded in blue), it requires a sulfur-containing reac-
tant and V4+ ions for organically templated vanadium selenites,
which is either introduced as a reagent or produced in situ.
The use of V(IV)OSO4 insures the generation of V4+. In com-
parison, amines with high polarizability (shaded in red) require
oxalates for success. The reason may be due to the charge den-
sity changed by the oxalate on the inorganic secondary building
units, matching the charge density of these long, linear, and
highly charged triamines and tetraamines. In addition, amines
with low polarizability (shaded in green) have a higher pKa
value than other amines and without the requirement of pH
<3 to be in the correct protonated state. These amines generate
Figure 13. SVM-derived decision tree for ML-guided synthesis based on failed experiments. Ovals, rectangles, and triangles represent decision nodes,
reaction-outcome bins, and excised subtrees, respectively. The shading of green, blue, and red indicate the three distinct successful groups, which are
corresponding to low- (<9.32 Å3), medium- (10.29–19.51 Å3), and high-polarizability (17.64–29.85 Å3) amines, respectively. Reproduced with permis-
sion.[92] Copyright 2015, Springer Nature.

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2110748 (18 of 25) © 2022 Wiley-VCH GmbH
enough V4+ from the V5+ precursor, but at a slower rate and
require a longer reaction time (>26 h). The usage of NaVO3
generally leads to the formation of inorganic-only polycrystal-
line products. However, use of NH4VO3 can exclude sodium
from the reaction mixture, enabling formation of the target
phase. Thus, through the SVM model, specific recommenda-
tions for compound formation are provided: i) Understanding
the production of appropriate primary building units (V4+);
ii) adjusting charge density to enable the matching between the
construction of secondary building units and the cationic com-
ponents; and iii) avoiding undesirable building units (Na+) that
result in non-templated phases. To this end, the ML approach
successfully exploited underlying pattern contained in historical
data and to elucidate the factors determining reaction products,
revealing previously unknown insights. It has universal guiding
significance for the synthesis of electrocatalyst.
Palkovits and co-workers also used several ML methods to
predict water-splitting catalysts based on published and original
data.[93] The ML models exhibited decent prediction accuracy,
confirming that even simple models were suitable for fore-
casting. Ahmed and co-workers joined high-throughput experi-
ments and ML-based regression models to guide Pt-group
metal-free electrocatalyst synthesis for ORR.[94] They developed
several ML-based regression models to predict ORR activity,
depending on selected synthesis control parameters (e.g., Fe
precursor identity, precursor content, and pyrolysis tempera-
ture). Based on the best gradient boosting regression and sup-
port-vector methods, the predicted candidates were obtained
with smaller root mean-square errors. Catalyst synthesis was
further performed. It was found that the advanced electro-
catalyst were obtained with 36% performance enhancement
compared to the original optimal ORR electrocatalyst. The suc-
cess of the combination of ML and experiment represented
a promising method for the development of high-efficiency
next-generation electrocatalysts. Additionally, Tapan and co-
workers used the decision tree analysis for CO2RR based on 471
experimental data points from 34 different publications.[95] The
results showed that the Faradaic efficiencies depend on the con-
tents of Sn, the type of catholyte, the potential applied and the
pH values. When the Sn content was higher than 15% and the
Cu content was lower than 52%, the selectivity of formic acid
was the highest for the most generalizable path. This showed
that exploratory data analysis and decision trees could provide
useful information to determine the high selectivity conditions
of CO2 electroreduction performance, to guide future research.
Furthermore, many applications for the catalyst charac-
terization such as, X-ray absorption fine structure,[96]
trans-
mission electron microscopy and scanning transmission
electron microscopy,[97]
energy-dispersive X-ray[98]
and electron
energy-loss[98a]
has been reported, which has been reviewed
previously.[7b]
Since these are not direct electrocatalyst design,
we will not repeat it here.
Collectively, the design of electrocatalysts through ML is still
in the preliminary stage based on experimental values. Neverthe-
less, it was found that the catalytic efficiency can be significantly
improved when the ML design was applied. In the future, if the
experimental values could be retrieved through a convenient
database, the ML modelling would be used before each experi-
ment. It will greatly save the time and cost of the experiment.
5. Challenges
ML and its combinatorial methods have been applied suc-
cessfully to electrocatalysts design, resulting in powerful tools
that are used to discover novel electrocatalysts while extracting
knowledge from extant datasets. Nevertheless, design chal-
lenges remain.
First, the lack of standard datasets for ML applications
limits its wider applicability. Although fast-developing big-data
mining technologies promise to extract useful information
and knowledge from large data pools,[5] data diversity limits
their scope. Currently, discovering and optimizing electrocata-
lysts are empirically driven. There is not enough relevant and
refined information to direct ML efforts. Although ML has
had success in many applications related to electrocatalysis,
ML-guided catalyst design remains in its initial stages. For
this reason, the data based on first principles/ab-initio, Monte
Carlo, molecular simulation, etc., calculations and experiments
for specific electrocatalytic reactions are required to uncover
the underlying patterns/rules. Moreover, the direct use of ML
techniques may result in discoveries of limited finely tuned
variations because that ML deduces predictive models that are
reflection of the existent training data. Therefore, only complete
datasets can provide reasonable results for ML predictions and
well-organized standard datasets are highly desired.
Secondly, how to efficiently draw the physical insights from
ML is also a huge challenge. To meet this challenge, the appro-
priate selection of descriptors, cross validation of ML methods,
and mutually ML verification of theoretical and experimental
data may be potential effective ways. Descriptors play a pivotal
role for electrocatalysis as they contain the essence of catalysis
from the physicochemical nature. Appropriate selection of
descriptors helps to capture the underlying physical pattern. As
aforementioned, the combination of structural, electronic, and
activity descriptors is a useful strategy to achieve ML applica-
tion. With the deepening of the understanding of descriptors,
there will be increasingly more ways for the precise selection
of descriptors in the future. Usually, the ML methods do not
incorporate physical laws determining the attribute, which
leads to uncertain error propagation within the models. Dif-
ferent ML models cross-validation can help reduce such
uncertainty. However, such cross-validation scheme requires a
sample that represents the full chemical space to be explored,
which is very difficult to obtain. To this end, the representative
sample is of critical importance. In addition, mutual ML veri-
fication of calculated and experimental data is also a potential
effective method to ensure the acquisition of physical inside.
The calculated data is often simplified due to the limited mod-
eling size and simulation range whereas the experimental data
often implies superposition laws due to the complex reaction
conditions. The consolidation of theoretical and experimental
data could be highly helpful to identify physical insight of elec-
trocatalyst and aid the future electrocatalyst.
Third, the lack of data in real electrocatalytic environment
for ML learning is a challenge for the real electrocatalyst pre-
diction. The ML learning examples are either from theoretical
calculation or from experiments. The calculations for the elec-
trocatalytic reaction at this stage are generally in a vacuum,
which is far from the real electrocatalytic environment. The

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electrocatalysis usually occurs at the solid–liquid interface. In
addition to the catalytic materials, there are solutions, elec-
trolytes, and applied voltages that participate in the reaction.
Therefore, the solvation effect, the polarization effect of the
electrocatalyst caused by the electrolyte effect and the applied
voltage, and the formation of the electric double layer are all
critical factors that need to be considered. Due to the super-
position of many factors, the reaction system is particularly
complicated. As a result, the understanding of the sold-liquid
interface is still very limited. Furthermore, there is also a lack of
understanding at the molecular level based on the experimental
observations due to the experimental limitation. As a result, the
simulation of the real environment of electrocatalysis by ML is
very limited due to shortage of learning examples.
In addition, the lack of standard methods and systemic guid-
ance for ML electrocatalysis applications is also a challenge.
Recent ML developments have resulted in powerful regres-
sion tools in various areas.[4] However, their efficiency depends
greatly on experience. For ML-assisted electrocatalysts, the skill
requirements for researchers are expansive, because the tech-
niques span domains such as computer science, mathematical
models, regression algorithms, and data mining. If a standard
systemic guidance for ML applications were to exist, it would
greatly promote ML applications across the board. In addition,
there is a lack of detailed guidance for electrocatalysis input
descriptors, resulting in high application barriers. Descriptors
determine input data and subsequently the training and design
results. Based on our systematic summary of descriptors and
their applications, it is obvious that there are neither universal
descriptors for HER, OER, ORR, CO2RR, and NRR reactions,
nor are there universal descriptors for specific materials. Iden-
tifying general descriptors needed for a specific reaction based
on specific materials is essential for the wider promotion and
application of MLs to electrocatalysis.
Another challenge is that empirical ML analysis on the
design of electrocatalysts is limited. Electrocatalysis is dynami-
cally determined by the chemical and structural properties of
active sites. These reactions are highly dependent on the tem-
perature, reactant concentrations, and flow rates, etc., experi-
mental conditions, as well as, other factors such as, material
structures and current densities. Thus, experimental data must
be produced under the comparable or even same conditions.
Combining data science with theoretical and experimental
methods is likely to lead to new ways to discover electrocata-
lysts. To increase the number of material data, researchers
should obtain theoretical metrics from high-throughput calcu-
lations so that they can result in intelligent methods.
6. Perspectives
With the growth and power of ML methods, electrocatalyst
applications will likely be extended to more systems. The
standard ML application mode would be achieved mostly in the
form of time-demanding and accurate calculations that would
not only focus on theoretical design but also on direct electroca-
talysis syntheses with high empirical coupling. ML applications
would not only focus on the aforementioned HER, OER, ORR,
CO2RR, and NRR paradigms, but they would also focus on
small molecular oxidations, such as, those of HOR,[99] MOR,[100]
EOR,[101] and Li-S batteries,[102] because these small molecules
are essential energy carriers for storage and conversion.
The deep coupling between experimental and computational
tools is solid in around electrocatalysis, owing to their comple-
mentary power. With the convenience and accuracy of first-
principles/ab-initio based theoretical calculations, ML could
perform high-throughput screening for complex catalytic sys-
tems to save experimental time and cost, which would facilitate
the automatic discovery of new scientific laws and principles
by allowing detailed inspections of the weights of trained ML
systems, providing transformational developments in science.[4]
Collaborations of experimenters and theoreticians have shown
great success in understanding electrocatalysis and new mate-
rial design. However, theoreticians and experimenters rarely
exchange original data, and data exchange typically occurs
long after the experiments/calculations have taken place. An
improved catalysis informatics strategy would have the poten-
tial to mitigate these limitations by improving data infrastruc-
tures and probabilistic frameworks via e-collaboration. The
rapid transfer of data and communications should be promoted
to facilitate rapid or even real-time integration of data from var-
ious theoretical and experimental sources in this mode.
Additionally, the application of ML in electrocatalysis is a
general trend in energy storage and conversion studies. First,
a plausible application is HOR, which produces a half-cell
reaction at an anode in a hydrogen fuel cell.[99b] In an acidic
environment, Pt catalysts are commonly used as electrocata-
lysts, whereas Pt and non-precious metals could be used as
electrocatalysts in alkaline environment.[103] However, the HOR
kinetics on Pt is about two orders of magnitude slower in an
alkali than in an acid.[104] Notwithstanding a couple of mecha-
nisms have been proposed for HORs in the alkaline environ-
ment, but low-HOR kinetics remain a key challenge.[105] The
development of inexpensive efficient catalysts for HOR is a
foundation to commercial deployments.[106] To date, the mate-
rials used for alkaline HOR media is limited, mostly nickel-
based materials.[99a,107] Moreover, these catalysts are facile to be
deactivated at high anode potentials, owing to the formation
of nickel hydroxide.[99a] In the future, MLs might be used to
design more efficient, more stable and cost-in-effective electro-
catalysts for alkaline fuel cells in addition to being as a powerful
tool for anion-exchange membranes designs.[108]
Direct methanol fuel cells (DMFCs) are among the most
promising alternative energy technologies,[101a]
owing to the
high energy density of methanol and the non-toxicity of CO2
and H2O.[100b,109]
Platinum is the most effective catalyst for
methanol oxidation reactions.[100b]
The key challenges lie in the
poor reactivity arising from CO poisoning and high cost of Pt
due to its scarcity, which hinders DMFC large-scale applica-
tions. A general strategy that might be used to meet these chal-
lenges includes the development of effective non-Pt, low-Pt,
and modified-Pt electrocatalysts.[109a]
ML applications are pos-
sible solutions for this. Furthermore, the selective oxidation of
methanol is a known alternative to the sluggish OER reaction
for water-splitting in anodes, which would significantly lower
the overpotential for H2 generation.[110]
For these reactions,
non-precious metals, such as, a nickel-based electrocatalysts,
would achieve high activity.[111]
However, long-term durability

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2110748 (20 of 25) © 2022 Wiley-VCH GmbH
remains a challenge for large-scale implementation.[110,112] ML-
based electrocatalyst design might enhance the activity and sta-
bility of electrocatalysts for commercial applications.
Similarly, direct ethanol fuel-cell (DEFC) technologies based
on ethanol oxidation have drawn increasing attention because
ethanol is a biomass fuel having low toxicity, renewability, and
a high theoretical energy density.[101b] The use of ethanol as a
DEFC fuel depends not only on its ease of production from
renewable sources but also upon its easy storage and trans-
portation.[113] To date, although a large number of research is
available for using Pt- and modified-Pt-based electrocatalysts
to facilitate ethanol oxidation,[114] the high cost, low conversion
efficiency, and inferior durability hinder DEFC commercializa-
tion.[115] Because EOR is a complex multiple-electron process
involving a couple of intermediates and products, an ML design
is highly desired to accelerate the electrocatalyst design.
Li-S batteries are cost-effective, they have high-energy
density, they are environmentally friendly and they offer high
safety,[102,116] making them among the most promising energy-
storage devices in a demanding market, particularly for electric
vehicles.[117] However, the insulating nature of active materials,
the Li-S shuttle effect, the slow redox kinetics and the Li den-
drite growth lead to a severe decay of capacity and low-rate
capabilities that hinder commercialization.[102a,116] The main dis-
advantage hindering the extensive application of Li-S batteries
lies in the severe leakage and migration of soluble lithium poly-
sulfide intermediates from the cathodes upon cycling.[118] The
use of metal compounds as electrocatalysts in Li-S systems,[119]
the use of phosphides to optimize Li-S chemistry[116] and defect
engineering[120] have been confirmed as effective strategies to
solve these problems. Despite these efforts, high performance
is still not available for commercial applications. ML applica-
tions for Li-S battery design will significantly improve this
situation.
Notwithstanding its limitations, ML is a data-driven design
that has been applied to electrocatalyst design and has shown
efficiency superior to traditional research methods. It aims to
discover relationships between multi-parameters and non-
dominant component-structure-processes in a complex system
of electrocatalysts. Although the prediction accuracy of MLs in
electrocatalyst discovery, design, performance, and application
has been greatly improved, the expansion of its transferability
is unimpressive. Active-learning methods rely on accuracy
and transferability. Moreover, discovering physically interpret-
able descriptors and penetrating black-box ML processes is a
hopeful prospect for data-driven material science. It would not
only assist with the design of new electrocatalysts, but it would
also allow people to understand the underlying physical laws
behind its properties while providing a theoretical basis for
the further design of electrocatalysts. With the development of
modern technologies, the requirements for new electrocatalysts
continue to grow. ML will undoubtedly play an increasing role
in their auxiliary design.
7. Conclusions
This review comprehensively summarized the ML applica-
tion progress in electrocatalyst design to date. To elucidate
the descriptor selective tactics, the geometrical, electronic,
and activity descriptors for the quantitative representation of
electrocatalysis were studied. It was found that the selection
of descriptors for ML application is highly dependent on the
reactions and associated properties. Additionally, to meet the
challenges of HER, OER, ORR, CO2RR, and NRR, the ML
applications in these areas were analyzed in detail. It was found
that the ML was a useful tool to reduce the loading of precious
metals but increase the activity for HER and ORR, as well as,
broke the scale relationship for the intermediates of ORR to
achieve low overpotentials. It was a useful tool for electrocata-
lyst design with low precious metals loading or non-precious
metals for HER, OER, ORR, CO2RR, and NRR. However, the
challenges remain due to the lack of standard datasets, standard
methods, and systemic guidance, which limits its wider appli-
cability. Moreover, there is a lack of detailed guidance for elec-
trocatalysis input descriptors, resulting in application barriers
for researchers. With the development of modern data science,
ML will undoubtedly play an increasing role in their auxil-
iary design, the potential application of the automated design,
discovery, and optimization are given for the well-known
electrocatalytic process of hydrogen, methanol, ethanol oxida-
tion reactions, as well as, sulphur oxidation reactions for Li-S
batteries.
Acknowledgements
The authors gratefully thank the financial support from the National
Natural Science Foundation of China (21975163), Shenzhen
Science and Technology Program (No. KQTD20190929173914967,
JCYJ20200109110416441), and the Senior Talent Research Start-up Fund
of Shenzhen University (000263 and 000265).
Conflict of Interest
The authors declare no conflict of interest.
Keywords
descriptors, electrocatalysis, high-throughput computations, machine
learning, structure–activity relationship
Received: October 23, 2021
Revised: December 22, 2021
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Jianwen Liu is a Research Professor in the College of Materials Science and Engineering,
Shenzhen University. He received a Ph.D. degree from the Chinese University of Hong Kong. His
current research interests focus on the theoretical studies of energy materials and their catalytic/
electrocatalytic properties using first principles calculations/ab initio molecular dynamics and
machine learning.

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Wenzhi Luo is a Research Assistant in the College of Materials Science and Engineering,
Shenzhen University. He received a M.S. degree from the Shantou University. His research
interests focus on DFT calculations of reaction mechanism and their applications in energy
materials.
Lei Wang is a Professor and the Dean of the College of Materials Science and Engineering
at Shenzhen University. He received his Ph.D. degree in Polymer Materials from Guangzhou
Institute of Chemistry, Chinese Academy of Sciences in 2006. His research interests mainly focus
on organic thermoelectric materials and proton exchange membrane for fuel cells.
Jiujun Zhang is a Professor at Shanghai University. He is a Principal Research Officer (Emeritus)
and Technical Core Competency Leader at the National Research Council of Canada Energy
(NRC). He received his Ph.D. in electrochemistry from Wuhan University in 1988 and carried out
postdoctoral research at the California Institute of Technology, York University, and the University
of British Columbia. He has over 30 years of scientific research experience, particularly in the
area of electrochemical energy storage and conversion. He is also the Adjunct Professor at the
University of British Columbia and the University of Waterloo.
Xian-Zhu Fu is currently a Professor in the College of Materials Science and Engineering,
Shenzhen University. He received his Ph.D. degree in Chemistry from Xiamen University in 2007.
Then he joined the Department of Materials and Chemical Engineering at University of Alberta
in Canada as a post-doctoral research fellow and Lawrence Berkeley National Lab as a visiting
scholar. From 2012–2017, he worked at the Shenzhen Institutes of Advanced Technology, Chinese
Academy of Sciences. His research interests focus on electrochemistry/electrocatalysts for energy
materials and devices, electronic materials, and process.

www.afm-journal.de
2110748 (25 of 25) © 2022 Wiley-VCH GmbH
Jing-Li Luo is a Distinguished Professor at Shenzhen University, China, Emeritus Professor at
University of Alberta, and Fellow of the Canadian Academy of Engineering. She obtained her
Ph.D. degree in Materials Science and Engineering from McMaster University, Canada in 1992.
She served as Canadian Research Chair in Alternative Fuel Cells from 2004 to 2015. Her research
focuses on fuel cells, energy storage research, clean energy technology, and corrosion control.

Adv Funct Materials - 2022 - Liu - Toward Excellence of Electrocatalyst Design by Emerging Descriptor‐Oriented Machine.pdf

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