Williamson Emily M, Brutchey Richard L
Department of Chemistry, University of Southern California, Los Angeles, California 90089, United States.
Inorg Chem. 2023 Oct 9;62(40):16251-16262. doi: 10.1021/acs.inorgchem.3c02697. Epub 2023 Sep 28.
The design of inorganic materials for various applications critically depends on our ability to manipulate their synthesis in a rational, robust, and controllable fashion. Different from the conventional trial-and-error approach, data-driven techniques such as the design of experiments (DoE) and machine learning are an effective and more efficient way to predictably control materials synthesis. Here, we present a Viewpoint on recent progress in leveraging such techniques for predicting and controlling the outcomes of inorganic materials synthesis. We first compare how the design choice (statistical DoE vs machine learning) affects the type of control it can offer over the resulting product attributes, information elucidated, and experimental cost. These attributes are supported by discussing select case studies from the recent literature that highlight the power of these techniques for materials synthesis. The influence of experimental bias is next discussed, followed finally by our perspectives on the major challenges in the widespread implementation of predictable and controllable materials synthesis using data-driven techniques.
用于各种应用的无机材料设计，关键取决于我们以合理、稳健且可控的方式操控其合成的能力。与传统的试错法不同，诸如实验设计（DoE）和机器学习等数据驱动技术，是一种可预测地控制材料合成的有效且更高效的方法。在此，我们针对利用此类技术预测和控制无机材料合成结果的最新进展发表一种观点。我们首先比较设计选择（统计实验设计与机器学习）如何影响其对所得产品属性、所阐明信息及实验成本所能提供的控制类型。通过讨论近期文献中的精选案例研究来支持这些属性，这些案例突出了这些技术在材料合成方面的力量。接下来讨论实验偏差的影响，最后是我们对使用数据驱动技术广泛实施可预测和可控材料合成面临的主要挑战的看法。
