用多精度贝叶斯优化加速化学分子筛选，提升效率。
Applying Multi-Fidelity Bayesian Optimization in Chemistry: Open Challenges and Major Considerations
- 融合高低精度数据，降低计算与实验成本
- 低精度数据在特定条件下显著提升搜索效率
- 适合材料与药物研发中资源受限的场景
多精度贝叶斯优化（MFBO）通过整合不同质量与资源成本的实验和计算数据，高效优化目标函数。该方法在化学发现中极具潜力，因其能有效融合多样数据源。本文研究了MFBO在加速识别有前景分子或材料中的应用，特别分析了低精度数据相较于单一精度方法提升性能的条件。针对两个关键挑战：最优采集函数的选择、成本与数据精度相关性的影响，进行了深入探讨，并讨论了评估MFBO在化学发现中有效性的方式。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。