Toward predicting surface energy of rutile TiO 2 with machine learning

作者：Fuming Lai, Riyue Ge, Min Zhao, Zhiling Zhou, Yanqiang Hu, Jian Yang, Shengfu Tong · 发表于：CrystEngComm · 年份：2022 · DOI：10.1039/d2ce01310j · 被引用次数：5 · 研究领域：Machine Learning in Materials Science、Electronic and Structural Properties of Oxides、Catalytic Processes in Materials Science

A database of rutile TiO 2 containing 3000 morphologies was established. With this database, the surface energy was predicted from the experimentally observed crystal equilibrium morphology using the KNN model.