Summary of Adaptive Catalyst Discovery Using Multicriteria Bayesian Optimization with Representation Learning, by Jie Chen et al.
Adaptive Catalyst Discovery Using Multicriteria Bayesian Optimization with Representation Learning
by Jie Chen, Pengfei Ou, Yuxin Chang, Hengrui Zhang, Xiao-Yan Li, Edward H. Sargent, Wei Chen
First submitted to arxiv on: 18 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computational Engineering, Finance, and Science (cs.CE); Chemical Physics (physics.chem-ph)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel computational approach for discovering high-performance catalysts is proposed by integrating density functional theory (DFT) with Bayesian Optimization (BO). The uncertainty-aware atomistic machine learning model, UPNet, enables automated representation learning from high-dimensional catalyst structures and principled uncertainty quantification. Within the BO framework, a constrained expected improvement acquisition function considers multiple evaluation criteria. This approach is applied to discover catalysts for the CO2 reduction reaction, achieving high prediction accuracy, facilitating interpretable feature extraction, and enabling multicriteria design optimization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to find better materials for energy conversion and healthcare is developed by using computer calculations. The method combines two techniques: density functional theory (DFT) and Bayesian Optimization (BO). A special type of AI model, called UPNet, helps learn from the materials’ structures and predict their properties. This approach can consider multiple factors at once, making it more effective than previous methods. It’s tested on finding catalysts for a reaction that reduces CO2, showing promising results. |
Keywords
* Artificial intelligence * Feature extraction * Machine learning * Optimization * Representation learning
