为材料与分子研究提供多保真贝叶斯优化的实用指南。
Best Practices for Multi-Fidelity Bayesian Optimization in Materials and Molecular Research
- 系统测试不同采集函数在合成问题中的表现
- 实证验证三类真实发现任务中多保真优于单保真
- 给出参数选择建议，助力化学领域落地应用
多保真贝叶斯优化（MFBO）可利用不同精度与成本的信息源加速材料与分子发现。尽管潜力巨大，但其在化学任务中的参数设置缺乏系统评估。本文针对分子与材料问题，测试两类采集函数在两个合成问题中的表现，分析近似函数的信息量与成本影响。基于自研实现与指导原则，对三个真实发现任务进行基准测试，并与单保真方法对比。结果可为化学科学中推广MFBO提供决策支持。
原文摘要 · Abstract (English)
Multi-fidelity Bayesian Optimization (MFBO) is a promising framework to speed up materials and molecular discovery as sources of information of different accuracies are at hand at increasing cost. Despite its potential use in chemical tasks, there is a lack of systematic evaluation of the many parameters playing a role in MFBO. In this work, we provide guidelines and recommendations to decide when to use MFBO in experimental settings. We investigate MFBO methods applied to molecules and materials problems. First, we test two different families of acquisition functions in two synthetic problems and study the effect of the informativeness and cost of the approximate function. We use our implementation and guidelines to benchmark three real discovery problems and compare them against their single-fidelity counterparts. Our results may help guide future efforts to implement MFBO as a routine tool in the chemical sciences.
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