面向自主材料实验室的贝叶斯优化：从算法到物理信息驱动的工作流程
Bayesian Optimization for Self-Driving Materials Laboratories: From Algorithms to Physics-Informed Workflows
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中文总结 AI 辅助
该综述针对材料自主实验室的实际挑战，介绍贝叶斯优化算法及物理信息驱动的贝叶斯优化，总结其在多类材料领域的应用成果，并展望其未来发展方向。
中文摘要 AI 辅助
自主实验室（SDLs）通过在合成、表征、数据分析与实验决策间形成闭环，正在变革材料研究。贝叶斯优化（BO）是这类闭环的决策引擎，它能基于稀缺且含噪声的数据选择实验，同时平衡利用与探索。然而，实际材料研发过程常偏离标准黑箱设定，涉及失败或缺失的实验、噪声与漂移、混合变量、约束、多目标、可变成本与保真度、历史数据迁移、批量或异步操作以及先验物理知识。本综述从这些实际挑战的视角，介绍面向材料自主实验室的贝叶斯优化。我们总结基于高斯过程的贝叶斯优化，以及将材料目标建模为量化目标的方法，随后讨论代理建模与获取函数中的主要选择。重点强调物理信息驱动的贝叶斯优化（PIBO），其中领域知识通过表征、先验、核函数、获取函数及约束融入。我们综述贝叶斯优化及相关主动学习方法在半导体、催化、化学反应、电池、合金、功能材料与量子材料领域取得的成果，突出参数优化之外的进展，包括新材料与合成路线、功能性能提升及可复用的科学知识。最后，我们概述贝叶斯优化驱动材料自主实验室的开放问题，包括非平稳性、多模态观测、自适应问题构建，以及人类、大语言模型与研究智能体的科学推理。解决这些挑战可能推动自主实验室从高效优化迈向可解释且能产生知识的实验。
英文摘要
Self-driving laboratories (SDLs) are transforming materials research by closing the loop among synthesis, characterization, data analysis and experimental decision making. Bayesian optimization (BO) is a decision engine for these loops because it can select experiments from scarce and noisy data while balancing exploitation and exploration. Yet real materials campaigns often depart from the standard black-box setting, involving failed or missing experiments, noise and drift, mixed variables, constraints, multiple objectives, variable cost and fidelity, transfer from historical data, batch or asynchronous operation, and prior physics knowledge. This review presents BO for materials SDLs through the lens of these practical challenges. We summarize Gaussian-process-based BO and the formulation of materials goals as quantitative objectives, then discuss major choices in surrogate modelling and acquisition. Particular emphasis is placed on physics-informed Bayesian optimization (PIBO), in which domain knowledge enters through representations, priors, kernels, acquisition functions, and constraints. We survey achievements enabled by BO and related active-learning approaches across semiconductors, catalysis, chemical reactions, batteries, alloys, functional materials and quantum materials, highlighting advances beyond parameter optimization, including new materials and synthesis routes, improved functional performance, and reusable scientific knowledge. We conclude by outlining open problems for BO-driven materials SDLs, including nonstationarity, multimodal observations, adaptive problem formulation, and scientific reasoning by humans, large language models and research agents. Addressing these challenges may advance SDLs beyond efficient optimization toward interpretable and knowledge-generating experimentation.
