3 Result(s)
within
Johannes Kastner
Computational Intelligence
Mathematical Modeling and Industrial Mathematics
Theoretical, Mathematical and Computational Physics
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Article
Open AccessEfficient training of ANN potentials by including atomic forces via Taylor expansion and application to water and a transition-metal oxide
Artificial neural network (ANN) potentials enable the efficient large-scale atomistic modeling of complex materials with near first-principles accuracy. For molecular dynamics simulations, accurate energies an...
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Article
Open AccessPerformance of two complementary machine-learned potentials in modelling chemically complex systems
Chemically complex multicomponent alloys possess exceptional properties derived from an inexhaustible compositional space. The complexity however makes interatomic potential development challenging. We explore...
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Article
Open AccessUncertainty-biased molecular dynamics for learning uniformly accurate interatomic potentials
Efficiently creating a concise but comprehensive data set for training machine-learned interatomic potentials (MLIPs) is an under-explored problem. Active learning, which uses biased or unbiased molecular dyna...
