625 citations · 1.4k across the 20 of their papers we have counts for
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Mechanical properties of single and polycrystalline solids from machine learning
Faridun N. Jalolov, Evgeny V. Podryabinkin, Artem R. Oganov +2
Calculations of elastic and mechanical characteristics of non-crystalline solids are challenging due to high computation cost of methods and low accuracy of empirical…
A machine learning potential-based generative algorithm for on-lattice crystal structure prediction
Vadim Sotskov, Alexander V. Shapeev, Evgeny V. Podryabinkin
We propose a method for crystal structure prediction based on a new structure generation algorithm and on-lattice machine learning interatomic potentials. Our algorithm generates t…
Accurate melting point prediction through autonomous physics-informed learning
Olga Klimanova, Timofei Miryashkin, Alexander Shapeev
We present an algorithm for computing melting points by autonomously learning from coexistence simulations in the NPT ensemble. Given the interatomic interaction model, the method…
MLIP-3: Active learning on atomic environments with Moment Tensor Potentials
Evgeny Podryabinkin, Kamil Garifullin, Alexander Shapeev +1
Nowadays, academic research relies not only on sharing with the academic community the scientific results obtained by research groups while studying certain phenomena, but also on…
Equivariant Tensor Network Potentials
Max Hodapp, Alexander Shapeev
Machine-learning interatomic potentials (MLIPs) have made a significant contribution to the recent progress in the fields of computational materials and chemistry due to the MLIPs'…