AI 中文总结
本研究将材料设计中的约束多目标贝叶斯优化问题，通过组合采集函数并采用UCB-Bandit和LLM驱动的多智能体控制器进行自适应策略选择，在模拟实验中优于固定策略，更适合约束材料科学问题。
AI 中文摘要
材料发现与设计活动可以表述为约束多目标贝叶斯优化（CMOBO）问题，其中每个实验决策都在两个相互耦合但相互竞争的目标之间进行权衡：发现可行候选材料和优化底层帕累托前沿。在此，我们将采集函数的选择重新构建为在常规采集函数和面向可行性的采集函数组合上的自适应策略选择问题。这通过两个控制器实现：UCB-Bandit（一种改进的UCB多臂老虎机）和Agentic-Switch（一种由大型语言模型（LLM）驱动的多智能体决策系统）。两者均在五个合成基准函数和两个材料设计案例研究中，与固定策略基线进行了计算机模拟评估。自适应策略在累计可行计数和可行超体积改进方面均表现出竞争力，而每个单独的采集函数仅在一个指标上表现良好，这表明自适应策略更适合约束材料科学问题。
英文摘要
Materials discovery and design campaigns can be formulated as constrained multi-objective Bayesian optimization (CMOBO) problems, within which each experimental decision negotiates between two coupled but competing goals: discovering feasible candidates and refining the underlying Pareto front. Here we recast acquisition-function choice as an adaptive policy-selection problem over a portfolio of conventional and feasibility-focused acquisition functions. This was done using two controllers: UCB-Bandit, a modified UCB multi-armed bandit, and Agentic-Switch, a multi-agent decision system driven by a large language model (LLM). Both were evaluated against fixed-policy baselines in silico across five synthetic benchmark functions and two materials design case studies. The adaptive policies performed competitively in terms of both cumulative feasibility count and feasible hypervolume improvement, while each individual acquisition function performed well for only one metric, suggesting that adaptive policies are better suited for constrained materials science problems.
