59 citations · 114 across the 5 of their papers we have counts for
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cond-mat.mtrl-sci2022★ 59 cited
Crystal structure prediction with machine learning-based element substitution
Minoru Kusaba, Chang Liu, Ryo Yoshida
The prediction of energetically stable crystal structures formed by a given chemical composition is a central problem in solid-state physics. In principle, the crystalline state of…
cond-mat.mtrl-sci2021★ 2 cited
Descriptors of intrinsic hydrodynamic thermal transport: screening a phonon database in a machine learning approach
Pol Torres, Stephen Wu, Shenghong Ju +4
Machine learning techniques are used to explore the intrinsic origins of the hydrodynamic thermal transport and to find new materials interesting for science and engineering. The h…
cond-mat.mtrl-sci2019★ 53 cited
Exploring diamond-like lattice thermal conductivity crystals via feature-based transfer learning
Shenghong Ju, Ryo Yoshida, Chang Liu +3
Ultrahigh lattice thermal conductivity materials hold great importance since they play a critical role in the thermal management of electronic and optical devices. Models using mac…