Accelerated chemical space search using a quantum-inspired cluster expansion approach

Hitarth Choubisa; Jehad Abed; Douglas Mendoza; Hidetoshi Matsumura; Masahiko Sugimura; Zhenpeng Yao; Ziyun Wang; Brandon R. Sutherland; Alán Aspuru-Guzik; Edward H. Sargent

doi:10.1016/j.matt.2022.11.031

Accelerated chemical space search using a quantum-inspired cluster expansion approach

Hitarth Choubisa, Jehad Abed, Douglas Mendoza, Hidetoshi Matsumura, Masahiko Sugimura, Zhenpeng Yao, Ziyun Wang, Brandon R. Sutherland, Alán Aspuru-Guzik^*, Edward H. Sargent^*

^*Corresponding author for this work

Chemistry

Research output: Contribution to journal › Article › peer-review

9 Scopus citations

Abstract

To enable the accelerated discovery of materials with desirable properties, it is critical to develop accurate and efficient search algorithms. Quantum annealers and similar quantum-inspired optimizers have the potential to provide accelerated computation for certain combinatorial optimization challenges. However, they have not been exploited for materials discovery because of the absence of compatible optimization mapping methods. Here, by combining cluster expansion with a quantum-inspired superposition technique, we lever quantum annealers in chemical space exploration for the first time. This approach enables us to accelerate the search of materials with desirable properties 10–50 times faster than genetic algorithms and bayesian optimizations, with a significant improvement in ground state prediction accuracy. We apply this to the discovery of acidic oxygen evolution reaction catalysts and find a promising previously unexplored chemical family of Ru-Cr-Mn-Sb-O₂. The best catalyst shows a mass activity eight times higher than state-of-the-art RuO₂ and maintains performance for 180 h.

Original language	English (US)
Pages (from-to)	605-625
Number of pages	21
Journal	Matter
Volume	6
Issue number	2
DOIs	https://doi.org/10.1016/j.matt.2022.11.031
State	Published - Feb 1 2023

Funding

This work was financially supported by Fujitsu Ltd. and Fujitsu Consulting (Canada) Inc. We thank Fujitsu for their generous access to the Digital Annealer where all the annealing operations and optimizations were performed. All the DFT calculations were performed on Compute Canada’s Niagara supercomputing cluster. The experimental work was supported financially by the Natural Sciences and Engineering Research Council of Canada (NSERC), Vanier Canada Graduate Scholarship. Theoretical work was supported through Hatch Scholarship for Sustainable Energy Research . Electron microscopy was conducted at the Ontario Center for the Characterization of Advanced Materials (OCCAM). This work was financially supported by Fujitsu Ltd. and Fujitsu Consulting (Canada) Inc. We thank Fujitsu for their generous access to the Digital Annealer where all the annealing operations and optimizations were performed. All the DFT calculations were performed on Compute Canada's Niagara supercomputing cluster. The experimental work was supported financially by the Natural Sciences and Engineering Research Council of Canada (NSERC), Vanier Canada Graduate Scholarship. Theoretical work was supported through Hatch Scholarship for Sustainable Energy Research. Electron microscopy was conducted at the Ontario Center for the Characterization of Advanced Materials (OCCAM). Conceptualization, A.A.G. and E.H.S.; methodology, H.C. J.A. D.M. H.M. M.S. Z.Y. and Z.W.; investigation, H.C. and J.A.; software, H.C. and H.M.; writing – original draft, H.C. and J.A.; writing – review & editing, E.H.S. A.A.G. and B.R.S.; funding acquisition, E.H.S. and Z.W.; resources, E.H.S.; supervision, E.H.S. and A.A.G. The authors declare no competing interests.

Keywords

MAP2: Benchmark
cluster expansion
density functional theory
machine learning
materials discovery
mixed multimetal oxides
oxygen evolution reaction
quantum annealers
quantum computing
quantum-inspired

ASJC Scopus subject areas

General Materials Science

Access to Document

10.1016/j.matt.2022.11.031

Cite this

@article{6858e4a8703649b8b319185b718ce010,

title = "Accelerated chemical space search using a quantum-inspired cluster expansion approach",

abstract = "To enable the accelerated discovery of materials with desirable properties, it is critical to develop accurate and efficient search algorithms. Quantum annealers and similar quantum-inspired optimizers have the potential to provide accelerated computation for certain combinatorial optimization challenges. However, they have not been exploited for materials discovery because of the absence of compatible optimization mapping methods. Here, by combining cluster expansion with a quantum-inspired superposition technique, we lever quantum annealers in chemical space exploration for the first time. This approach enables us to accelerate the search of materials with desirable properties 10–50 times faster than genetic algorithms and bayesian optimizations, with a significant improvement in ground state prediction accuracy. We apply this to the discovery of acidic oxygen evolution reaction catalysts and find a promising previously unexplored chemical family of Ru-Cr-Mn-Sb-O2. The best catalyst shows a mass activity eight times higher than state-of-the-art RuO2 and maintains performance for 180 h.",

keywords = "MAP2: Benchmark, cluster expansion, density functional theory, machine learning, materials discovery, mixed multimetal oxides, oxygen evolution reaction, quantum annealers, quantum computing, quantum-inspired",

author = "Hitarth Choubisa and Jehad Abed and Douglas Mendoza and Hidetoshi Matsumura and Masahiko Sugimura and Zhenpeng Yao and Ziyun Wang and Sutherland, {Brandon R.} and Al{\'a}n Aspuru-Guzik and Sargent, {Edward H.}",

note = "Funding Information: This work was financially supported by Fujitsu Ltd. and Fujitsu Consulting (Canada) Inc. We thank Fujitsu for their generous access to the Digital Annealer where all the annealing operations and optimizations were performed. All the DFT calculations were performed on Compute Canada{\textquoteright}s Niagara supercomputing cluster. The experimental work was supported financially by the Natural Sciences and Engineering Research Council of Canada (NSERC), Vanier Canada Graduate Scholarship. Theoretical work was supported through Hatch Scholarship for Sustainable Energy Research . Electron microscopy was conducted at the Ontario Center for the Characterization of Advanced Materials (OCCAM). Funding Information: This work was financially supported by Fujitsu Ltd. and Fujitsu Consulting (Canada) Inc. We thank Fujitsu for their generous access to the Digital Annealer where all the annealing operations and optimizations were performed. All the DFT calculations were performed on Compute Canada's Niagara supercomputing cluster. The experimental work was supported financially by the Natural Sciences and Engineering Research Council of Canada (NSERC), Vanier Canada Graduate Scholarship. Theoretical work was supported through Hatch Scholarship for Sustainable Energy Research. Electron microscopy was conducted at the Ontario Center for the Characterization of Advanced Materials (OCCAM). Conceptualization, A.A.G. and E.H.S.; methodology, H.C. J.A. D.M. H.M. M.S. Z.Y. and Z.W.; investigation, H.C. and J.A.; software, H.C. and H.M.; writing – original draft, H.C. and J.A.; writing – review & editing, E.H.S. A.A.G. and B.R.S.; funding acquisition, E.H.S. and Z.W.; resources, E.H.S.; supervision, E.H.S. and A.A.G. The authors declare no competing interests. Publisher Copyright: {\textcopyright} 2022 Elsevier Inc.",

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AU - Choubisa, Hitarth

AU - Abed, Jehad

AU - Mendoza, Douglas

AU - Matsumura, Hidetoshi

AU - Sugimura, Masahiko

AU - Yao, Zhenpeng

AU - Wang, Ziyun

AU - Sutherland, Brandon R.

AU - Aspuru-Guzik, Alán

AU - Sargent, Edward H.

N1 - Funding Information: This work was financially supported by Fujitsu Ltd. and Fujitsu Consulting (Canada) Inc. We thank Fujitsu for their generous access to the Digital Annealer where all the annealing operations and optimizations were performed. All the DFT calculations were performed on Compute Canada’s Niagara supercomputing cluster. The experimental work was supported financially by the Natural Sciences and Engineering Research Council of Canada (NSERC), Vanier Canada Graduate Scholarship. Theoretical work was supported through Hatch Scholarship for Sustainable Energy Research . Electron microscopy was conducted at the Ontario Center for the Characterization of Advanced Materials (OCCAM). Funding Information: This work was financially supported by Fujitsu Ltd. and Fujitsu Consulting (Canada) Inc. We thank Fujitsu for their generous access to the Digital Annealer where all the annealing operations and optimizations were performed. All the DFT calculations were performed on Compute Canada's Niagara supercomputing cluster. The experimental work was supported financially by the Natural Sciences and Engineering Research Council of Canada (NSERC), Vanier Canada Graduate Scholarship. Theoretical work was supported through Hatch Scholarship for Sustainable Energy Research. Electron microscopy was conducted at the Ontario Center for the Characterization of Advanced Materials (OCCAM). Conceptualization, A.A.G. and E.H.S.; methodology, H.C. J.A. D.M. H.M. M.S. Z.Y. and Z.W.; investigation, H.C. and J.A.; software, H.C. and H.M.; writing – original draft, H.C. and J.A.; writing – review & editing, E.H.S. A.A.G. and B.R.S.; funding acquisition, E.H.S. and Z.W.; resources, E.H.S.; supervision, E.H.S. and A.A.G. The authors declare no competing interests. Publisher Copyright: © 2022 Elsevier Inc.

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N2 - To enable the accelerated discovery of materials with desirable properties, it is critical to develop accurate and efficient search algorithms. Quantum annealers and similar quantum-inspired optimizers have the potential to provide accelerated computation for certain combinatorial optimization challenges. However, they have not been exploited for materials discovery because of the absence of compatible optimization mapping methods. Here, by combining cluster expansion with a quantum-inspired superposition technique, we lever quantum annealers in chemical space exploration for the first time. This approach enables us to accelerate the search of materials with desirable properties 10–50 times faster than genetic algorithms and bayesian optimizations, with a significant improvement in ground state prediction accuracy. We apply this to the discovery of acidic oxygen evolution reaction catalysts and find a promising previously unexplored chemical family of Ru-Cr-Mn-Sb-O2. The best catalyst shows a mass activity eight times higher than state-of-the-art RuO2 and maintains performance for 180 h.

AB - To enable the accelerated discovery of materials with desirable properties, it is critical to develop accurate and efficient search algorithms. Quantum annealers and similar quantum-inspired optimizers have the potential to provide accelerated computation for certain combinatorial optimization challenges. However, they have not been exploited for materials discovery because of the absence of compatible optimization mapping methods. Here, by combining cluster expansion with a quantum-inspired superposition technique, we lever quantum annealers in chemical space exploration for the first time. This approach enables us to accelerate the search of materials with desirable properties 10–50 times faster than genetic algorithms and bayesian optimizations, with a significant improvement in ground state prediction accuracy. We apply this to the discovery of acidic oxygen evolution reaction catalysts and find a promising previously unexplored chemical family of Ru-Cr-Mn-Sb-O2. The best catalyst shows a mass activity eight times higher than state-of-the-art RuO2 and maintains performance for 180 h.

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KW - density functional theory

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KW - materials discovery

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KW - quantum annealers

KW - quantum computing

KW - quantum-inspired

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Accelerated chemical space search using a quantum-inspired cluster expansion approach

Abstract

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Keywords

ASJC Scopus subject areas

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