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Deep dive into machine learning density functional theory for materials science and chemistry
With the growth of computational resources, the scope of electronic structure simulations has
increased greatly. Artificial intelligence and robust data analysis hold the promise to …
increased greatly. Artificial intelligence and robust data analysis hold the promise to …
Machine learning for polymeric materials: an introduction
Polymers are incredibly versatile materials and have become ubiquitous. Increasingly,
researchers are using data science and polymer informatics to design new materials and …
researchers are using data science and polymer informatics to design new materials and …
The MLIP package: moment tensor potentials with MPI and active learning
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 using …
interatomic potentials, rather than science (the'what'and'why') of atomistic simulations using …
On-the-fly active learning of interatomic potentials for large-scale atomistic simulations
The on-the-fly generation of machine-learning force fields by active-learning schemes
attracts a great deal of attention in the community of atomistic simulations. The algorithms …
attracts a great deal of attention in the community of atomistic simulations. The algorithms …
MLIP-3: Active learning on atomic environments with moment tensor potentials
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 …
scientific results obtained by research groups while studying certain phenomena but also on …
Machine learning assisted discovery of high-efficiency self-healing epoxy coating for corrosion protection
Abstract Machine learning is a powerful means for the rapid development of high-
performance functional materials. In this study, we presented a machine learning workflow …
performance functional materials. In this study, we presented a machine learning workflow …
Dynamical strengthening of covalent and non-covalent molecular interactions by nuclear quantum effects at finite temperature
Nuclear quantum effects (NQE) tend to generate delocalized molecular dynamics due to the
inclusion of the zero point energy and its coupling with the anharmonicities in interatomic …
inclusion of the zero point energy and its coupling with the anharmonicities in interatomic …
Nanohardness from first principles with active learning on atomic environments
We propose a methodology for the calculation of nanohardness by atomistic simulations of
nanoindentation. The methodology is enabled by machine-learning interatomic potentials …
nanoindentation. The methodology is enabled by machine-learning interatomic potentials …
Couplings for Andersen dynamics
Andersen dynamics is a standard method for molecular simulations, and a precursor of the
Hamiltonian Monte Carlo algorithm used in MCMC inference. The stochastic process …
Hamiltonian Monte Carlo algorithm used in MCMC inference. The stochastic process …
Active learning for snap interatomic potentials via bayesian predictive uncertainty
Bayesian inference with a simple Gaussian error model is used to efficiently compute
prediction variances for energies, forces, and stresses in the linear SNAP interatomic …
prediction variances for energies, forces, and stresses in the linear SNAP interatomic …