Machine-learning-assisted thin-film growth: Bayesian optimization in molecular beam epitaxy of SrRuO3 thin films
Machine-learning-assisted thin-film growth: Bayesian optimization in molecular beam epitaxy of SrRuO3 thin films
复制标题
机器学习辅助薄膜生长：SrRuO3 薄膜分子束外延的贝叶斯优化
DOI：
10.1063/1.5123019
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发表时间：
2019
期刊：
影响因子：
6.1
通讯作者：
Hideki Yamamoto
中科院分区：
文献类型：
--
作者：
Y. Wakabayashi;Takuma Otsuka;Y. Krockenberger;H. Sawada;Y. Taniyasu;Hideki Yamamoto
利用机器学习技术的材料信息学，例如，贝叶斯优化（BO）有可能通过根据新测量的数据增量更新机器学习模型来提供薄膜生长条件的高通量优化。在这里，我们展示了基于BO的分子束外延（MBE）的SrRuO 3，在氧化物电子学的研究领域中最深入研究的材料之一，主要是由于其独特的性质作为一种铁磁金属。为了简化纠缠增长条件的复杂搜索空间，我们对单个条件运行BO，同时保持其他条件不变。其结果是，高结晶质量的SrRuO 3膜表现出高的剩余电阻率比（RRR）超过50，以及强的垂直磁各向异性的开发，在只有24 MBE生长运行中，其中Ru通量率，生长温度，和O3-坩埚到衬底的距离进行了优化。我们基于BO的搜索方法提供了一种高效的实验设计，不依赖于单个研究人员的经验和技能，它减少了实验时间和成本，这将加速材料研究。
Materials informatics exploiting machine learning techniques, e.g., Bayesian optimization (BO), has the potential to offer high-throughput optimization of thin-film growth conditions through incremental updates of machine learning models in accordance with newly measured data. Here, we demonstrated BO-based molecular beam epitaxy (MBE) of SrRuO3, one of the most-intensively studied materials in the research field of oxide electronics, mainly owing to its unique nature as a ferromagnetic metal. To simplify the intricate search space of entangled growth conditions, we ran the BO for a single condition while keeping the other conditions fixed. As a result, high-crystalline-quality SrRuO3 film exhibiting a high residual resistivity ratio (RRR) of over 50 as well as strong perpendicular magnetic anisotropy was developed in only 24 MBE growth runs in which the Ru flux rate, growth temperature, and O3-nozzle-to-substrate distance were optimized. Our BO-based search method provides an efficient experimental design that is not as dependent on the experience and skills of individual researchers, and it reduces experimental time and cost, which will accelerate materials research.