224 citations · 606 across the 7 of their papers we have counts for
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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…
Prediction of C7N6 and C9N4: Stable and strong porous carbon-nitride nanosheets with attractive electronic and optical properties
Bohayra Mortazavi, Masoud Shahrokhi, Alexander V Shapeev +2
In this work, three novel porous carbon-nitride nanosheets with C7N6, C9N4 and C10N3 stoichiometries are predicted. First-principles simulations were accordingly employed to evalua…
A Performance and Cost Assessment of Machine Learning Interatomic Potentials
Yunxing Zuo, Chi Chen, Xiangguo Li +8
Machine learning of the quantitative relationship between local environment descriptors and the potential energy surface of a system of atoms has emerged as a new frontier in the d…
Moment tensor Potentials as a Promising Tool to Study Diffusion Processes
I. I. Novoselov, A. V. Yanilkin, A. V. Shapeev +1
A recently proposed class of machine-learning interatomic potentials --- Moment tensor potentials (MTPs) --- is investigated in this work. MTPs are able to actively select configur…