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

Submission Track: Findings & Open Challenges

Submission Category: AI-Guided Design

Keywords: Multi-fidelity BO, molecular discovery, material discovery, bayesian optimization

TL;DR: We apply multi-fidelity Bayesian optimization to four case studies, including molecular discovery, and discuss the limitations and challenges.

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.

AI4Mat Journal Track: Yes

Submission Number: 33