npj: 贝叶斯优化—加速筛选钙钛矿和相关溶剂
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新材料研发过程中，人们越来越重视通过机器学习技术来实现快速搜索、设计。
制备太阳能电池薄膜的所谓有机-无机杂化钙钛矿（HOIP）材料的最终溶液组分如何配制，从超过500,000种可能的阳离子、卤化物和溶剂混合物的组合中，筛选出恰当组合以制备出具有所需特性的HOIP，靠实验研究或分子模拟进行的搜索代价高昂。来自美国亚利桑那大学Matthias Poloczek教授等提出了一种改进的贝叶斯优化方法，用于预测各种HOIP和溶剂的最佳组合。他们以分子间结合能筛选了240种可能的钙钛矿-溶剂组合，发现FAPbI2Cl和四氢噻吩1-氧化物的分子间结合能最大。与先前最先进的贝叶斯优化方法相比，采用特种应用程序的内核来克服数据稀缺之类的困难，显着降低了计算成本。作者所报道的改进方法，可扩展应用于对混合离子和混合卤化物/阳离子系统的研究，也可应用于解决广泛材料的设计和优化问题。
该文近期发表于npj Computational Materials 4: 51 (2018) ，英文标题与摘要如下，点击左下角“阅读原文”可以自由获取论文PDF。
Efficient search of compositional space for hybrid organic–inorganic perovskites via Bayesian optimization
Henry C. Herbol, Weici Hu, Peter Frazier, Paulette Clancy & Matthias Poloczek
Accelerated searches, made possible by machine learning techniques, are of growing interest in materials discovery. A suitable case involves the solution processing of components that ultimately form thin films of solar cell materials known as hybrid organic–inorganic perovskites (HOIPs). The number of molecular species that combine in solution to form these films constitutes an overwhelmingly large “compositional” space (at times, exceeding 500,000 possible combinations). Selecting a HOIP with desirable characteristics involves choosing different cations, halides, and solvent blends from a diverse palette of options. An unguided search by experimental investigations or molecular simulations is prohibitively expensive. In this work, we propose a Bayesian optimization method that uses an application-specific kernel to overcome challenges where data is scarce, and in which the search space is given by binary variables indicating whether a constituent is present or not. We demonstrate that the proposed approach identifies HOIPs with the targeted maximum intermolecular binding energy between HOIP salt and solvent at considerably lower cost than previous state-of-the-art Bayesian optimization methodology and at a fraction of the time (less than 10%) needed to complete an exhaustive search. We find an optimal composition within 15 ± 10 iterations in a HOIP compositional space containing 72 combinations, and within 31 ± 9 iterations when considering mixed halides (240 combinations). Exhaustive quantum mechanical simulations of all possible combinations were used to validate the optimal prediction from a Bayesian optimization approach. This paper demonstrates the potential of the Bayesian optimization methodology reported here for new materials discovery.
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