Summary of Applying Multi-fidelity Bayesian Optimization in Chemistry: Open Challenges and Major Considerations, by Edmund Judge et al.
Applying Multi-Fidelity Bayesian Optimization in Chemistry: Open Challenges and Major Considerations
by Edmund Judge, Mohammed Azzouzi, Austin M. Mroz, Antonio del Rio Chanona, Kim E. Jelfs
First submitted to arxiv on: 11 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Materials Science (cond-mat.mtrl-sci); 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 The paper presents a method called Multi Fidelity Bayesian Optimization (MFBO) that efficiently optimizes towards desired outcomes by leveraging experimental and computational data of varying quality and resource cost. This approach is attractive for chemical discovery, where MFBO can integrate diverse data sources to accelerate the identification of promising molecules or materials. The authors investigate the conditions under which lower fidelity data can enhance performance compared to single-fidelity problem formulations, addressing challenges such as selecting optimal acquisition functions, understanding cost and data fidelity correlation, and assessing effectiveness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops a way to quickly find new molecules or materials that are useful for chemical discovery. It uses a technique called Bayesian Optimization (BO) to search through many different possibilities and find the best ones. The authors want to know when using less accurate data can actually help with this process, and they explore ways to make it work well. |
Keywords
» Artificial intelligence » Optimization
