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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'…
Bayesian learning of thermodynamic integration and numerical convergence for accurate phase diagrams
V. Ladygin, I. Beniya, E. Makarov +1
Accurate phase diagram calculation from molecular dynamics requires systematic treatment and convergence of statistical averages. In this work we propose a Gaussian process regress…
The MLIP package: Moment Tensor Potentials with MPI and Active Learning
Ivan S. Novikov, Konstantin Gubaev, Evgeny V. Podryabinkin +1
The subject of this paper is the technology (the "how") of constructing machine-learning interatomic potentials, rather than science (the "what" and "why") of atomistic simulations…
Nanoporous C3N4, C3N5 and C3N6 nanosheets; Novel strong semiconductors with low thermal conductivities and appealing optical/electronic properties
Bohayra Mortazavi, Fazel Shojaei, Masoud Shahrokhi +4
Carbon nitride two-dimensional (2D) materials are among the most attractive class of nanomaterials, with wide range of application prospects. As a continuous progress, most recentl…
In operando active learning of interatomic interaction during large-scale simulations
Max Hodapp, Alexander Shapeev
A well-known drawback of state-of-the-art machine-learning interatomic potentials is their poor ability to extrapolate beyond the training domain. For small-scale problems with ten…