3 citations · 4 across the 2 of their papers we have counts for
2 papers
cond-mat.mtrl-sci2024★ 1 cited
Elemental Reactivity Maps for Materials Discovery
Yuki Inada, Masaya Fujioka, Haruhiko Morito +3
When searching for novel inorganic materials, limiting the combination of constituent elements can greatly improve the search efficiency. In this study, we used machine learning to…
cond-mat.mtrl-sci2023★ 3 cited
DeepCrysTet: A Deep Learning Approach Using Tetrahedral Mesh for Predicting Properties of Crystalline Materials
Hirofumi Tsuruta, Yukari Katsura, Masaya Kumagai
Machine learning (ML) is becoming increasingly popular for predicting material properties to accelerate materials discovery. Because material properties are strongly affected by it…