This document summarizes the work of Zachary Ulissi and his research group on developing computational workflows and machine learning methods to accelerate catalyst discovery. Key points include: - The group is generating large datasets of catalyst descriptors like adsorption energies to train graph neural network models that can predict properties and screen for new catalysts. - Their GASpy software coordinates density functional theory calculations using dependency graphs to efficiently generate data. - Preliminary graph convolution models show accuracy comparable to DFT for predicting adsorption energies and surface energies. - The group is applying these methods to problems like oxygen evolution reaction catalyst screening to potentially find new active compositions beyond what is searched manually.

1. Zachary Ulissi
Assistant Professor, Chemical Engineering
Carnegie Mellon University
zulissi@andrew.cmu.edu
ulissigroup.cheme.cmu.edu
Enabling Data Science Methods
for Catalyst Design and Discovery
1

2. 2
Seh et al Science 2017

3. CO2
Reduction Electrochemistry
H2
Cn
Hx
Oy
CO2
Anode: 2H2
O → O2
+ 4(H+
+ e-
) Eo
~ 1.23V
Cathode: CO2
+ m(H+
+ e-
) → Cx
Hy
Oz
+ nH2
O Eo
~ 0V
2(H+
+ e-
) → H2
Eo
= 0V

4. Descriptors for CO2
Electrochemistry
4
Liu et al. Nature Communications 2017
CurrenttoCH4orC2+
hydrocarbons

5. Relaxation coordinate
energy
5
Example: low-coverage thermochemistry
relaxation

6. Increasing complexity in catalyst discovery
6
Pure Surfaces
Overlayers,
surf. alloys
A3
B Alloys,
other prototypes
Stable alloys,
facets beyond (100),(111)
Screening at reaction
conditions / surface
coverages
Screening for
beneficial defects or
segregation
Screening
complex nanoparticle
geometries
Hansen et al 2016
Xin et al 2017
Ulissi et al 2018
Kim et al 2018
Greeley et al 2006
Mixed
Materials
(oxides, metals, etc)
Near future
1) Dynamic modular workflows for data generation and organization
in catalysis
2) Graph convolutional models in catalysis

7. 7
Direct Enumeration of Possible Calculations
bulk
composition
Low-index
surfaces
adsorption
site
adsorbate
x15,000,000
x10,000
x200,000

8. Catalyst Composition Screening for CO2
RR
8
Tran, Ulissi Nature Catalysis 2018

9. Robust Intermetallics
9tSNE dimensionality reduction from full coordination fingerprint space to two dimensions

10. Dependency graphs to coordinate tasks
10
GASpy (software development w/ LUIGI
https://github.com/ulissigroup/GASpy)
Tran, Ulissi et al. JCIM 2018
Define calculations:
- Pre-requisite
calculations
- How to complete
calculation if
needed
- Processing steps

11. Dependency graphs to coordinate tasks
11
GASpy (software development,
https://github.com/ulissigroup/GASpy)
Tran, Ulissi et al. JCIM 2018
Vibrational free energy
correction (ASE)

12. Dependency graphs to coordinate tasks
12
GASpy (software development,
https://github.com/ulissigroup/GASpy)
Tran, Ulissi et al. JCIM 2018
Vibrational free energy
correction (ASE)
Surface Energy

13. Dependency graphs to coordinate tasks
13
GASpy (software development,
https://github.com/ulissigroup/GASpy)
Tran, Ulissi et al. JCIM 2018
Vibrational free energy
correction (ASE)
Surface Energy
“I want the adsorption
energy of *CO on a Cu-Cu
bridge site on CuPd(111)”

14. Dependency graphs to coordinate tasks
14
GASpy (software development,
https://github.com/ulissigroup/GASpy)
Tran, Ulissi et al. JCIM 2018
Vibrational free energy
correction (ASE)
Surface Energy
“I want energies for CO
and H on all possible sites
on NiGa(110)”

15. ML / Active Optimization Automation
15
DFT Calculation
Database
Catalog of Possible
Calculations
Build model for *CO
Build model for *OH
Every night:
Build models
Every night: predict
properties for every
entry in catalog
Every 2 hours:
Check queues, schedule
calculations
Design of
Experiments
...

16. GASpy: current modules
• Adsorption energies (with multiple configurations)
• Surface energies
• Enum./databases of possible catalysts and sites
• Coordination-based fingerprints
• Daily ML model fitting, prediction, DOE
• Daily volcano plot analysis
Under development:
• Graph convolution prediction methods
• Solvation/electrolyte corrections to adsorption energies
(w. Joel Varley, LLNL)
• Adsorbate configuration/rotation degrees of freedom
16

17. Surface Science Datasets
1. Common catalyst site descriptors
(*CO, *H, *O, *OH, *OOH, *C, …)
a. ~100,000 site descriptors across
30+ elements, 1,500 crystals, 20,000 surfaces
b. 20,000 each *CO/*H already open/published
2. Surface energy / stability
a. ~2,000 surface energies across various
compositions, low-index facets, etc17

18. Graph Convolution Methods for Surface Energy
Aini PalizhatiWen Zhong
DFT energy
slab thickness
surface
energy
Xie & Grossman, Physical Review Letters (2018)
(Paper in draft)

19. Graph Convolution Methods for Adsorption
19
Nianhan Tian, Seoin Back, Kevin Tran, Junwoong Yoon, Zachary Ulissi. JPCL 2019
ΔE
Seoin Back
Kevin Tran
Kaylee Tian

20. Graph Convolution Methods for Adsorption
20
Nianhan Tian, Seoin Back, Kevin Tran, Junwoong Yoon, Zachary Ulissi. JPCL 2019
ΔE
Seoin Back
Kevin Tran
Kaylee Tian
Cheating!!

21. Graph Convolution Methods for Adsorption
21
Nianhan Tian, Seoin Back, Kevin Tran, Junwoong Yoon, Zachary Ulissi. JPCL 2019
ΔE

22. Graph Convolution Methods for Adsorption
22
Nianhan Tian, Seoin Back, Kevin Tran, Junwoong Yoon, Zachary Ulissi. JPCL 2019
ΔE

23. Graph Convolution Methods for Adsorption
23
Nianhan Tian, Seoin Back, Kevin Tran, Junwoong Yoon, Zachary Ulissi. JPCL 2019
ΔE

24. Graph Convolution Methods for Adsorption
24
dE(CO)
Coordination-based model
Nianhan Tian, Seoin Back, Kevin Tran, Junwoong Yoon, Zachary Ulissi. JPCL 2019
DFT accuracy
DFT Training Structures

25. Oxygen Evolution Reaction (OER)
25
Anodic reaction in water-splitting cells: 2H2
O -> O2
+ 4(H+
+ e-
)
Assumptions in screen:
- Stable structures
- No reconstruction
- (001) facet or similar is most active
Conclusions:
- IrOx
is not active
- SrIrO3
is not active
- Few active IrO2
surfaces
Jaramillo, Vojvodic et al. Science 2016

26. Oxygen Evolution Reaction (OER)
26
Anodic reaction in water-splitting cells: 2H2
O -> O2
+ 4(H+
+ e-
)
Missing: other IrOx polymorphs/facets
Jaramillo, Vojvodic et al. Science 2016

27. Challenges
1. In screening for stable compounds and
surfaces, are we missing other candidates?
2. How should we look for new surfaces?
3. Human cost of each calculation is very high
27
Bulk Relaxation
(+U determin.)
Surface
Enumeration
(facets)
Stable Surface
Termination
Active Site
Determination
Each point is O(months) in
graduate student time

28. Workflows for Oxide Chemistry
28
~11 Intensive DFT
calculations per
facet direction

29. Workflow reproduces known points and
predictions polymorph activity
29

30. Same CGCNN models fit stability/activity
30

31. Conclusions
● Surface chemistry is open to similar
automation + prediction as for bulks
● Surface challenges will always be data-poor
○ need active learning / active optimization
● Building ML models requires care in what
data representation and model testing
We’re hiring: please get in touch for post-doc
positions! 31

32. 32