复杂量子材料的主动学习分子束外延
Active learning molecular beam epitaxy of complex quantum materials
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中文总结 AI 辅助
该研究针对二元量子材料MBE的相边界与窄生长窗口问题，提出结合随机森林代理模型与期望改进函数的SMBO主动学习框架，在Fe₃Sn外延中4次迭代使预测误差减半至约10%，为量子材料自主合成提供了数据高效方案。
中文摘要 AI 辅助
将机器学习（ML）整合到材料科学中，为实现完全自主的合成工作流程提供了变革性途径。对于分子束外延（MBE）这类精密薄膜沉积技术而言，这种自动化至关重要，可克服耗时的手动探索高维热力学相空间的问题。现有的机器学习辅助薄膜生长方法主要依赖连续贝叶斯优化（BO）模型，这些模型假设参数空间是平滑的，因此难以捕捉二元量子材料固有的 abrupt 晶体学相边界和狭窄生长窗口。在此，我们展示了一种专门针对这类化合物的闭环MBE设计的基于序列模型的优化（SMBO）主动学习协议。为克服连续模型的局限性，同时保留传统BO高效的探索-利用逻辑，我们结合了能够捕捉高度非线性相变和生长过程热力学约束的随机森林代理模型，以及期望改进函数来预测最佳生长参数。我们将此组合SMBO框架应用于拓扑外尔铁磁体Fe₃Sn的MBE，该材料以亚稳线性化合物形式存在。使用少于20次生长迭代的小型初始训练集，我们的主动学习循环快速探索复杂的优化空间，以确定由尖锐跃迁界定的最佳生长窗口。仅在4次主动学习迭代内，绝对预测误差就减半至约10%。这种数据高效的框架为功能量子材料的自主发现和薄膜合成铺平了道路。
英文摘要
The integration of machine learning (ML) into materials science offers a transformative pathway toward fully autonomous synthesis workflows. For precise thin-film deposition techniques like molecular beam epitaxy (MBE), this automation is critical to overcome the time-consuming, manual navigation of high-dimensional thermodynamic phase spaces. Existing approaches for ML-assisted thin film growth predominantly rely on continuous Bayesian optimization (BO) models that assume smooth parameter landscapes. Consequently, they struggle to capture the abrupt crystallographic phase boundaries and narrow growth windows inherent to binary quantum materials. Here, we demonstrate an active learning protocol based on Sequential Model-Based Optimization (SMBO) designed specifically for the closed-loop MBE of such compounds. To overcome the limitations of continuous models while retaining the efficient exploration-exploitation logic of traditional BO, we combine a random forest surrogate model capable of capturing highly non-linear phase transitions and thermodynamics constraints of the growth process with an expected improvement function to predict optimum growth parameters. We apply this combined SMBO framework to the MBE of the topological Weyl ferromagnet Fe$_3$Sn, which exists as a metastable line compound. Using a small initial training set of fewer than twenty growth iterations, our active learning loop rapidly navigates a complex optimization landscape to identify an optimum growth window bounded by sharp transitions. Within only four active learning iterations, the absolute predictive error is halved to $\approx10\%$. This data-efficient framework paves the way for the autonomous discovery and thin-film synthesis of functional quantum materials.
