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arXiv 2602.05350cond-mat.mtrl-sci
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融合物理知识的获取加权方法用于氧化物薄膜生长的 stoichiometry 限制的贝叶斯优化

Physics-informed acquisition weighting for stoichiometry-constrained Bayesian optimization of oxide thin-film growth

NTT, Inc.(日本电信电话株式会社)

Yuki K. Wakabayashi, Takuma Otsuka, Yoshiharu Krockenberger, Yoshitaka Taniyasu

AI总结:

该研究提出一种融合物理知识的贝叶斯优化方法,通过加权函数约束优化方向,实现氧化物薄膜生长的高效化学计量优化。

摘要:

我们提出了一种融合物理知识的贝叶斯优化(PIBO),通过对其获取函数的简洁修改来纳入物理先验知识。具体来说,该方法将预期改进(EI)乘以一个编码先验晶体生长物理的权重。当应用于LaAlO3分子束外延时,权重函数定义了一个平坦的化学计量窗口,并惩罚窗口外的提案,从而将优化引导至物理上合理区域,同时保持受控的探索。在闭环优化中,与纯EI相比,加权EI将搜索限制在化学计量区域,同时保留足够的灵活性以探索邻近条件,最终找到一个略微超出化学计量窗口的最优解。在仅15次生长运行中,所生长的LaAlO3薄膜的晶格常数收敛到体材料值,证明了该方法在理想化学计量生长中的高效和快速优化。由于物理知识仅通过权重函数纳入,该方法仅需对标准BO工作流程进行最小修改,并可轻松应用于其他材料系统,提供了一条通用且实用的AI驱动材料合成途径。

英文摘要:

We present a physics-informed Bayesian optimization (PIBO) with a concise modification to its acquisition function to incorporate the physical prior knowledge. Specifically, this method multiplies the expected improvement (EI) by a weight encoding prior crystal growth physics. When applied to LaAlO3 molecular-beam epitaxy, the weighting function defines a flat stoichiometric window and penalizes off-window proposals, thereby steering the optimization toward physically plausible regions while maintaining controlled exploration. In a closed-loop optimization, relative to the bare EI, which often proposes off-stoichiometric conditions, the weighted EI constrains the search toward stoichiometric regions while retaining sufficient flexibility to explore neighboring conditions, eventually identifying an optimum slightly beyond the stoichiometric window. Within only 15 growth runs, the lattice constant of the grown LaAlO3 film converged to the bulk value, evidencing efficient and rapid optimization for the ideal stoichiometric growth. Because physics knowledge is incorporated solely through the weighting function, the approach requires only minimal modification to standard BO workflows and is readily applicable to other material systems, offering a general and practical route to AI-driven materials synthesis.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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