Applying Multi-Fidelity Bayesian Optimization in Chemistry: Open Challenges and Major Considerations

Edmund Judge, Mohammed Azzouzi, Austin M. Mroz, Antonio del Rio Chanona, Kim E. Jelfs
9/11/2024
cs.LGcond-mat.mtrl-sciphysics.chem-ph

No official code found. Best implementation: mohammedazzouzi15/STK_search (1 star, MIT, last commit 2025-08-07).

Abstract

Multi fidelity Bayesian optimization (MFBO) leverages experimental and or computational data of varying quality and resource cost to optimize towards desired maxima cost effectively. This approach is particularly attractive for chemical discovery due to MFBO's ability to integrate diverse data sources. Here, we investigate the application of MFBO to accelerate the identification of promising molecules or materials. We specifically analyze the conditions under which lower fidelity data can enhance performance compared to single-fidelity problem formulations. We address two key challenges, selecting the optimal acquisition function, understanding the impact of cost, and data fidelity correlation. We then discuss how to assess the effectiveness of MFBO for chemical discovery.

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mohammedazzouzi15/STK_searchCommunity100%
MIT

Implementation of a search algorithm based on STK

10CSS, HTMLSep 20, 20231 years agoMIT

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Cite this paper

@article{judge2024applying,
  title  = {Applying Multi-Fidelity Bayesian Optimization in Chemistry: Open Challenges and Major Considerations},
  author = {Edmund Judge and Mohammed Azzouzi and Austin M. Mroz and Antonio del Rio Chanona and Kim E. Jelfs},
  year   = {2024},
  eprint = {2409.07190},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2409.07190v1}
}

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