Adaptive Catalyst Discovery Using Multicriteria Bayesian Optimization with Representation Learning
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866915612284944384 |
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| author | Chen, Jie Ou, Pengfei Chang, Yuxin Zhang, Hengrui Li, Xiao-Yan Sargent, Edward H. Chen, Wei |
| author_facet | Chen, Jie Ou, Pengfei Chang, Yuxin Zhang, Hengrui Li, Xiao-Yan Sargent, Edward H. Chen, Wei |
| contents | High-performance catalysts are crucial for sustainable energy conversion and human health. However, the discovery of catalysts faces challenges due to the absence of efficient approaches to navigating vast and high-dimensional structure and composition spaces. In this study, we propose a high-throughput computational catalyst screening approach integrating density functional theory (DFT) and Bayesian Optimization (BO). Within the BO framework, we propose an uncertainty-aware atomistic machine learning model, UPNet, which enables automated representation learning directly from high-dimensional catalyst structures and achieves principled uncertainty quantification. Utilizing a constrained expected improvement acquisition function, our BO framework simultaneously considers multiple evaluation criteria. Using the proposed methods, we explore catalyst discovery for the CO2 reduction reaction. The results demonstrate that our approach achieves high prediction accuracy, facilitates interpretable feature extraction, and enables multicriteria design optimization, leading to significant reduction of computing power and time (10x reduction of required DFT calculations) in high-performance catalyst discovery. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_12445 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Adaptive Catalyst Discovery Using Multicriteria Bayesian Optimization with Representation Learning Chen, Jie Ou, Pengfei Chang, Yuxin Zhang, Hengrui Li, Xiao-Yan Sargent, Edward H. Chen, Wei Machine Learning Computational Engineering, Finance, and Science Chemical Physics High-performance catalysts are crucial for sustainable energy conversion and human health. However, the discovery of catalysts faces challenges due to the absence of efficient approaches to navigating vast and high-dimensional structure and composition spaces. In this study, we propose a high-throughput computational catalyst screening approach integrating density functional theory (DFT) and Bayesian Optimization (BO). Within the BO framework, we propose an uncertainty-aware atomistic machine learning model, UPNet, which enables automated representation learning directly from high-dimensional catalyst structures and achieves principled uncertainty quantification. Utilizing a constrained expected improvement acquisition function, our BO framework simultaneously considers multiple evaluation criteria. Using the proposed methods, we explore catalyst discovery for the CO2 reduction reaction. The results demonstrate that our approach achieves high prediction accuracy, facilitates interpretable feature extraction, and enables multicriteria design optimization, leading to significant reduction of computing power and time (10x reduction of required DFT calculations) in high-performance catalyst discovery. |
| title | Adaptive Catalyst Discovery Using Multicriteria Bayesian Optimization with Representation Learning |
| topic | Machine Learning Computational Engineering, Finance, and Science Chemical Physics |
| url | https://arxiv.org/abs/2404.12445 |