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1. Realization of Innovative Light Energy
Conversion Materials utilizing the
Supercomputer Fugaku
the 4th R-CCS International Symposium
7 February 2022 15:00–15:20JST
RIKEN Center for Computational Science, Kobe, Japan
Takahito Nakajima

2.  The challenge issues towards the effective use
of “Fugaku” were selected and their target
software programs were developed as part of
the FS2020 project (MEXT, FY2014–2019).
 FS2020 included 9 priority issues and 4
exploratory challenges selected from various
research fields.
Issue 5: Development of new fundamental
technologies for highly-efficient energy creation,
conversion, storage and use (Leader: Prof. Okazaki)
There were 3 research branches driving the research
activities toward the goal:
 Creation and storage of alternative energy resources -
Solar cells and artificial photosynthesis
 Conversion and storage of energy - Fuel cells and
rechargeable batteries
 Effective use of energy and material resources -
Methane, CO2, and efficient catalysts
FLAGSHIP 2020 Project

3.  In Issue 5, four application programs were selected as infrastructure programs that effectively
utilize “Fugaku” to contribute solutions to renewable energy problems.
Application Software in Issue 5
Applications Functionalities Performance on “K”
1 NTChem
Nakajima group
General-purpose quantum chemistry software
Ground and excited state calculations for 10,000
atom systems
World’s largest level of parallel QC computations
360 atoms, 9,840 atomic orbitals
80,199 nodes, 34% parallel efficiency
2 GELLAN
Tenno group
Hierarchical quantum chemistry software
Highly-accurate excited state calculations with
more than 1020
electron configurations
World's highest level of accurate QC calculations
120 atoms, 5,520 atomic orbitals
21,672 nodes, 32% parallel efficiency
3 MODYLAS
Okazaki group
General-purpose classical MD software
Large-scale MD simulations for billion-atom
systems
One of the fastest MD simulations in the world
80 million atoms
65,536 nodes, 41% parallel efficiency
4 stat-CPMD
Tateyama group
First-principles MD software
Reaction free energy calculation for 5,000 atom
systems
Efficient and reliable chemical reaction simulations
2,400 atomic systems
3,840 nodes, 29% parallel efficiency

4.  NTChem is a high-performance software package for molecular electronic
structure calculations for general purpose on various supercomputers and
workstations, and is a useful tool in various computational studies for large
and complicated molecular systems.
 NTChem is a community-based software package. We intend to continue
adopting users’ requests with the aim of making the program more
convenient and user-friendly for researchers in various research fields.
User cases
・Toyobo ・Mitsubishi Chem. ・RIKEN ・Hokkaido U. ・U. Tokyo ・Kyoto U. ・Kobe U. ・Yokohama-
City U. ・Gifu U. ・JAEA ・Open to the public on K and SC centers (IMS, FOCUS, Nagoya U., Kyusyu
U., Tokyo Tech.)
http://www.r-ccs.riken.jp/software_center/software/ntchem/
NTChem

5.  NTChem is designed to carry out high performance calculations on computers
with tens of thousands of nodes for DFT, TDDFT, MP2, and QMC.
 The good parallel scaling was achieved with our new algorithm on “K” and “Fugaku”.
Elapsed time was 11 mins using 80,199 nodes of “K” for nanographene (C150H30)2. The
world’s largest wavefunction-based electron-correlation calculation (Left Figure)
 By using the same MP2 algorithm on “K”, we can predict, with high accuracy, the
property known as the heat of formation of large fullerenes. Our finding suggests that
new interesting properties that show the character of both fullerenes and graphenes
appear if much larger fullerenes are synthesized. (Right Figure)
Parallel Computing with NTChem
Calculated heats of formation per carbon (kJ mol–1)
0
17822
35644
53466
71288
0 17822 35644 53466 71288
Speedups
Ideal
3.5 PFLOPS
(34 %efficiency)

6. NTChem on Fugaku
 We are currently developing a new version of “NTChem”. It adopts a memory-
distributed sparse-matrix algorithm based on “NTPoly” to overcome available
memory sizes per a compute node. It also adopts an algorithm with regular
communication patterns that map to high dimensional torus network architectures to
manage significant irregular data movement.
 We hope that NTChem will realize ab initio QC calculations for large molecules
including 1M atoms on “Fugaku” because the new version is capable of calculating
molecules with 10K atoms on “K” in the current stage.
 In future, we will perform novel computational applications for realistic molecular
systems such as whole photosystem II (1.2M atoms) on “Fugaku”.
Old
New
11,682atoms

7.  The successor to the priority issues in FS2020 is a “Program for Promoting
Research on the Supercomputer Fugaku” (MEXT, FY2020–2022, FY2021–2025).
 The project includes the four focus areas:
 Future development and challenges to general human issues
 Strengthening the strategies to protect the lives and assets of citizens
 Strengthening the competitiveness of industry
 Research foundation
with 22 selected projects.
Program for Promoting Researches on Fugaku

8.  to realize the social implementation of innovative light energy conversion materials by utilizing
massive materials simulations and informatics on “Fugaku”
Realization of Innovative Light Energy Conversion Materials utilizing the
Supercomputer Fugaku in Program for Promoting Research on Fugaku
Leader: Takahito Nakajima (RIKEN)
Objectives
Realization of Innovative
Photocatalyst for Hydrogen
Production
Kobe U・NAIST
Countermeasures against
Infectious Diseases by Virus
Inactivation
Kobe U・Kyoto U
Novel Materials Design for High
Efficiency Lead-Free Perovskite
Solar Cells
RIKEN・Kyoto U・ENEOS
Expected Achievements
 Realization of hydrogen-generating photocatalysts with the world’s highest conversion efficiency of
over 10% in collaboration with industries
 Social implementation of photocatalysts that inactivate infectious viruses and generate hydrogen
peroxide for disinfection selectively and efficiently
 Realization of Pb-free perovskite solar cells with a conversion efficiency of >30% surpassing Si-based
cells

9.  We plan to realize accelerating the discovery of novel materials such as
photovoltaics, photocatalysts, polymer batteries, and advanced biopolymers
by establishing novel materials informatics techniques that assimilate large
databases and mathematics in materials science with high-throughput
simulations on “Fugaku”.
 We also have several plans to collaborate with experimental groups and
industries to discover new materials that are highly efficient, low-cost,
environmentally clean, and sustainable. We would like to realize novel
material designs by co-designing experiments, theorizing, simulations, and
informatics.
Materials Informatics on Fugaku

10. Perovskite Solar Cells
 Perovskites with small organic molecules are one of the promising new-
generation solar cells, which are expected to generate renewable resources
and resolve global energy problems. The perovskite solar cells achieve the
power conversion efficiency of beyond 25% (25.5%, 2021.10.11).
 The most prominent perovskite in photovoltaic applications is a type of
perovskite containing toxic Pb such as MAPbI3 and FAPbI3.

11.  To search for novel Pb-free perovskite solar cells containing non-toxic and
widely available metals, we performed a systematic high-throughput first-
principles simulation on the K computer for 11,025 compositions of
perovskites in ABX3 and A2BB’X6 forms, where A is an organic or inorganic
component, B/B’ is a metal atom, and X is a halogen atom.
 By applying a screening procedure to all the computed compounds, we
discovered novel candidates for environmentally friendly Pb-free perovskite
solar cells and proposed 51 low-toxic halide perovskites, most of which are
proposed newly in our work.
Materials Design for PSCs
 The potential candidates are
categorized as only 6 types based
on the combination of groups to
which two metal elements (at B-
and Bʹ-sites) belong in the periodic
table; group 14–14, group 13–15,
group 11–11, group 9–13, group
11–13, group 11–15.
Press release: http://www.riken.jp/pr/press/2017/20171005_1/

12. Our Fugaku Promoting Research Project
 We plan to perform more massive high-throughput simulations for a huge
number of compounds totaling more than 10 million, employing a
comprehensive combination of elements with a higher degree of freedom
than our previous research to build a large-scale materials database for PSCs.
 We will propose candidates for Pb-free PSCs through efficient search with
screening and demonstrate novel materials by providing the results to the
cooperative experimental groups and the related companies.
 The feature is to design novel materials not only by simulations but also by
materials informatics. We will construct a prediction model to estimate
conversion efficiency and stability through machine learning based on the
materials database to propose candidates for highly efficient and stable Pb-
free PSCs.

13. Hole-Transport Materials for PSCs
 Spiro-OMeTAD is the most frequently used hole-transport material (HTM) for PSCs.
PSCs with spiro-OMeTAD yield the high PCE. For example, the PCE of
(FAPbI3)0.92(MAPbBr3)0.08 with spiro-OMeTAD is 23.4% [Yoo et al. (2019)].
 To improve the PCE, the derivatives of spiro-OMeTAD are implemented. For example,
the PCE of FAPbI3 with spiro-mF is 24.82% [Jeong et al. (2020)].
 However, spiro-OMeTAD and its derivative are prohibitively expensive ($274/g for
spiro-OMeTAD) because of their multistep synthesis and low yields. Cheaper HTMs
have been found with comparably high PCEs (X60: $120/g 19.6%, Py-C: $192/g 12.4%,
etc), but cost-effective materials with high PCE of over 20% are yet scarce.
 Recently, we discovered novel candidates for HTMs of PSCs which can be alternatives
with high PCE to spiro-OMeTAD by using materials informatics and simulations.
spiro-OMeTAD, SAscore: 3.95 spiro-mF, Sascore: 4.40

14. Data collection
 We collected experimental data for PCEs of PSCs: 88 perovskites with VBM and CBM,
326 HTMs, 10 ETMs with CBM, 10 dopants, 28 co-dopants, and 50 active areas. The
total number of datasets is 712.
Preparation of candidate molecules and fragments
 Every HTM in the database was decomposed to 3 fragments. The new compositions
for HTMs were built by combining 3 fragments selected from the prepared fragment
sets.
Materials Design of Novel HTMs
Database
・Perovskites with VBM and CBM
・HTMs ・ETMs with CBM
・Dopants ・Co-dopants
・Active areas

15. Molecular descriptors
 The topological and geometrical molecular descriptors for each fragment were
calculated by Mordred.
 The quantum-chemical electronic descriptors for each fragment were evaluated by
NTChem: HOMO, LUMO, total energies, electronic energies, heats of formation,
dispersion energies, and dipole moments.
Materials Design of Novel HTMs
Spiro-OMeTAD:
COC1=CC=C(C=C1)N(C2=CC=C(C=C2)OC)C3=CC4=C(C=C3)C5=C(C46C7=C(C=CC
(=C7)N(C8=CC=C(C=C8)OC)C9=CC=C(C=C9)OC)C1=C6C=C(C=C1)N(C1=CC=C(C=
C1)OC)C1=CC=C(C=C1)OC)C=C(C=C5)N(C1=CC=C(C=C1)OC)C1=CC=C(C=C1)OC
・HOMO ・LUMO
・Total energy
・Electronic energy
・Heat of formation
・Dispersion energy
・Dipole moment

16. Prediction of candidates for HTMs
 To predict a HTM with the highest PCE, we adopted a prediction scheme with the
Gaussian process regression (GPR) model (Bayesian optimization), which improves
uncertainty and predictability by considering the error variance of prediction values.
 To construct this GPR model, the classical and quantum molecular descriptors are
provided as inputs. PCEs with the error variance are predicted as outputs from the
GPR model.
Materials Design of Novel HTMs
Bayesian optimization
CM/QM descriptors are provided
as inputs. PCEs with the error
variance are predicted as outputs.

17. Selection of candidates for HTMs
 To obtain the optimal set of fragments which compose a candidate molecule, the
acquisition function, obtained from the GPR model, was optimized by particle swarm
optimization (PSO). PSO was used to tackle the optimization problem in the vast
chemical space, where the number of all compositions is over 90,000,000 in this study.
 The suitable experiment conditions for the fixed active area (0.2 cm2 in this study)
were also simultaneously determined from among the combinations of collected
experimental data.
Materials Design of Novel HTMs
GPR + PSO
Search for HTM with highest PCE.
Optimization of the improvement
function is done by PSO.

18. Virtual experiments
 We performed a virtual experiment in our approach. In this step, we adopted the
different prediction model rather than the GPR model. That is, PCEs of HTMs
proposed by the GPR model were determined by the deep neural network (DNN)
model.
 The prediction value obtained from the DNN model was added as the input for the
GPR model. As a result, both GPR and DNN models were reconstructed by repeating
the prediction and the virtual experiment so that the most likely HTM was selected by
the improved model in every step.
Materials Design of Novel HTMs
DNN
Search for HTM with highest PCE.
Optimization of the improvement
function is done by PSO.
Virtual experiment
GPR + PSO
PCEs of HTMs proposed
by GPR are determined
by DNN.

19. Construction of the DNN model
 For the construction of the DNN model, similar to the construction of the GPR model,
the classical and quantum molecular descriptors and the experimental conditions are
provided as inputs, and PCEs are predicted as outputs.
 To construct the accurate DNN model, 3 different prediction models (partial least
squares regression, support vector machine, k-nearest neighbors) were prebuilt and
their prediction values for PCEs were added to the inputs for the DNN model.
Materials Design of Novel HTMs
DNN
PCEs of HTMs proposed
by GPR are determined
by DNN.

20. Materials-Informatics-Driven Design of HTMs
 We carried out 20 independent virtual experiments with 100 updates per run.
 We have identified 2 novel candidates for HTMs of PSCs that yield high PCEs and are
considered to be easier to synthesize than spiro-OMeTAD (SAscore: 3.95).
Proposed candidates for HTMs
24.4%, SAscore: 3.74 24.0%, SAscore: 3.48

21. Acknowledgements
 This work was supported by MEXT as “Program for Promoting Researches on
the Supercomputer Fugaku” (Realization of innovative light energy
conversion materials utilizing the supercomputer Fugaku, Grant Number
JPMXP1020210317).
 This work was supported by FOCUS Establishing Supercomputing Center of
Excellence.
 This work was supported by Cabinet Office, Government of Japan, Cross-
ministerial Strategic Innovation Promotion Program (SIP), “Technologies for
Smart Bio-industry and Agriculture” (funding agency: Bio-oriented
Technology Research Advancement Institution, NARO).