DACOMSIN 2021. Session 1. Machine Learning for Materials Science
0:12 Yi Liu (Shanghai University). Machine Learning Accelerated Multicomponent Alloy Design
31:59 Nadezhda N. Kiselyova (IMET RAS); Victor A. Dudarev (NRU HSE); Andrey V. Stolyarenko (IMET RAS). Machine Learning Application to Predict New Inorganic Compounds – Results and Perspectives
51:24 Matteo Baldoni; Fabio Le Piane; Francesco Mercuri (CNR-ISMN). Artificial intelligence, machine learning and data-enabled multiscale simulation workflows for the design and evelopment of molecular materials
1:10:17 Jakob Ropers, Marco M. Mosca, Olga Anosova, Vitaliy Kurlin, and Andrew I. Cooper (University of Liverpool). Fast predictions of lattice energies by continuous isometry invariants of crystal structures
1:29:39 Faridun Jalolov; Artem Oganov; Alexander G Kvashnin (Skolkovo Institute of
Science and Technology). Simulations of mechanical properties of materials by using machine learning interatomic potentials
1:45:34 Johan Bratberg, Vice President, Thermo-Calc Software AB. The role of CALPHAD-bases tools in filling the materials data gaps for materials 4.0.