NTT Device Technology Labs, NTT Corporation, 3-1 Morinosato, Wakamiya, Atsugi, Kanagawa, 243-0198, Japan.
Sci Rep. 2022 Jan 18;12(1):930. doi: 10.1038/s41598-022-05010-7.
Scientists who design chemical substances often use materials informatics (MI), a data-driven approach with either computer simulation or artificial intelligence (AI). MI is a valuable technique, but applying it to layered structures is difficult. Most of the proposed computer-aided material search techniques use atomic or molecular simulations, which are limited to small areas. Some AI approaches have planned layered structures, but they require a physical theory or abundant experimental results. There is no universal design tool for multilayer films in MI. Here, we show a multilayer film can be designed through machine learning (ML) of experimental procedures extracted from chemical-coating articles. We converted material names according to International Union of Pure and Applied Chemistry rules and stored them in databases for each fabrication step without any physicochemical theory. Compared with experimental results which depend on authors, experimental protocol is superiority at almost unified and less data loss. Connecting scientific knowledge through ML enables us to predict untrained film structures. This suggests that AI imitates research activity, which is normally inspired by other scientific achievements and can thus be used as a general design technique.
科学家在设计化学物质时，通常会使用材料信息学（MI），这是一种数据驱动的方法，既可以通过计算机模拟，也可以通过人工智能（AI）实现。MI 是一种很有价值的技术，但将其应用于层状结构却很困难。大多数提出的计算机辅助材料搜索技术都使用原子或分子模拟，而这些模拟方法的适用范围有限。一些 AI 方法已经规划了层状结构，但它们需要物理理论或丰富的实验结果。在 MI 中，没有用于多层膜的通用设计工具。在这里，我们展示了一种可以通过从化学涂层文章中提取的实验程序的机器学习（ML）来设计多层膜的方法。我们根据国际纯粹与应用化学联合会的规则转换了材料名称，并将其存储在每个制造步骤的数据库中，而无需任何物理化学理论。与依赖于作者的实验结果相比，实验方案具有几乎统一和数据丢失更少的优势。通过 ML 连接科学知识使我们能够预测未经训练的薄膜结构。这表明 AI 模仿了研究活动，而研究活动通常是受到其他科学成果的启发，因此可以用作通用设计技术。
