448 citations · 1.8k across the 31 of their papers we have counts for
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CRYSPNet: Crystal Structure Predictions via Neural Network
Haotong Liang, Valentin Stanev, A. Gilad Kusne +1
Structure is the most basic and important property of crystalline solids; it determines directly or indirectly most materials characteristics. However, predicting crystal structure…
Materials development by interpretable machine learning
Yuma Iwasaki, Ryoto Sawada, Valentin Stanev +7
Machine learning technologies are expected to be great tools for scientific discoveries. In particular, materials development (which has brought a lot of innovation by finding new…
Machine-learning guided discovery of a high-performance spin-driven thermoelectric material
Yuma Iwasaki, Ichiro Takeuchi, Valentin Stanev +10
Thermoelectric conversion using Seebeck effect for generation of electricity is becoming an indispensable technology for energy harvesting and smart thermal management. Recently, t…
Unsupervised Phase Mapping of X-ray Diffraction Data by Nonnegative Matrix Factorization Integrated with Custom Clustering
Valentin Stanev, Velimir V. Vesselinov, A. Gilad Kusne +3
Analyzing large X-ray diffraction (XRD) datasets is a key step in high-throughput mapping of the compositional phase diagrams of combinatorial materials libraries. Optimizing and a…