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Found 4 papers, 2 papers with code
Phase discovery with active learning: Application to structural phase transitions in equiatomic NiTi
Here, we train four machine-learned force fields for equiatomic NiTi based on the LDA, PBE, PBEsol, and SCAN DFT functionals.
Multitask machine learning of collective variables for enhanced sampling of rare events
Computing accurate reaction rates is a central challenge in computational chemistry and biology because of the high cost of free energy estimation with unbiased molecular dynamics.
Bayesian Force Fields from Active Learning for Simulation of Inter-Dimensional Transformation of Stanene
We present a way to dramatically accelerate Gaussian process models for interatomic force fields based on many-body kernels by mapping both forces and uncertainties onto functions of low-dimensional features.
On-the-Fly Bayesian Active Learning of Interpretable Force-Fields for Atomistic Rare Events
Machine learning based interatomic potentials currently require manual construction of training sets consisting of thousands of first principles calculations and are often restricted to single-component and nonreactive systems.
Computational Physics Materials Science
