AI/Data Informatics: Computational Model Development, Validation, and Uncertainty Quantification: On-Demand Poster Session
Sponsored by: TMS Materials Processing and Manufacturing Division, TMS: Computational Materials Science and Engineering Committee
Program Organizers: Saurabh Puri, Microstructure Engineering; Dennis Dimiduk, BlueQuartz Software LLC; Darren Pagan, Pennsylvania State University; Anthony Rollett, Carnegie Mellon University; Francesca Tavazza, National Institute of Standards and Technology; Christopher Woodward, Air Force Research Laboratory

Monday 8:00 AM
March 14, 2022
Room: Materials Design
Location: On-Demand Poster Hall

Applicability Domain for Prediction Models of Thermoelectric Properties Based on Similarity to Known Materials: Masaya Kumagai1; Yukari Katsura2; Yuki Ando2; Atsumi Tanaka3; Koji Tsuda3; Ken Kurosaki1; 1Kyoto University; 2National Institute for Materials Science; 3The University of Tokyo
    Machine learning (ML) has attracted attention for material exploration and design. In general, randomly extracted subsets as unknown materials are used to evaluate the performance of ML models. However, in many cases, the details of subsets are not mentioned. Therefore, it is unclear how extent the model is applicable to unknown materials in the actual vast material space. In this study, we constructed ML models to predict thermoelectric properties from feature vectors based on chemical composition, and evaluated the performance of the ML models after clarifying the unknown materials by the following two ways: 1. material family classification of unknown materials based on clustering of known materials, 2. determination of applicability domain for the ML models based on its similarity to known materials.

Direct Prediction of Mechanical Properties from X-ray Diffraction Patterns Using Machine Learning: Naoki Hato1; Masaya Kumagai1; Ken Kurosaki1; 1Kyoto University
    The development of new materials requires many steps, such as sample preparation, X-ray diffraction measurement and analysis, property measurement, and performance evaluation, and requires an enormous amount of time. In recent years, high-throughput material synthesis and high-speed X-ray diffraction measurements have made sample preparation and X-ray diffraction measurements more efficient. However, the process from crystal structure analysis to performance evaluation has not yet become efficient. In this study, we attempted to predict mechanical properties directly from X-ray diffraction patterns using machine learning. Specifically, we used an optimal feature vector based on the X-ray diffraction pattern to predict the crystal structure and mechanical properties that are highly related to the crystal structure.