107 citations · 417 across the 22 of their papers we have counts for
4 papers · 2 filters
Thermally Averaged Magnetic Anisotropy Tensors via Machine Learning Based on Gaussian Moments
Viktor Zaverkin, Julia Netz, Fabian Zills +2
We propose a machine learning method to model molecular tensorial quantities, namely the magnetic anisotropy tensor, based on the Gaussian-moment neural-network approach. We demons…
Predicting Properties of Periodic Systems from Cluster Data: A Case Study of Liquid Water
Viktor Zaverkin, David Holzmüller, Robin Schuldt +1
The accuracy of the training data limits the accuracy of bulk properties from machine-learned potentials. For example, hybrid functionals or wave-function-based quantum chemical me…
Uncertainty-biased molecular dynamics for learning uniformly accurate interatomic potentials
Viktor Zaverkin, David Holzmüller, Henrik Christiansen +5
Efficiently creating a concise but comprehensive data set for training machine-learned interatomic potentials (MLIPs) is an under-explored problem. Active learning, which uses bias…
Transfer learning for chemically accurate interatomic neural network potentials
Viktor Zaverkin, David Holzmüller, Luca Bonfirraro +1
Developing machine learning-based interatomic potentials from ab-initio electronic structure methods remains a challenging task for computational chemistry and materials science. T…