Fuchs, Paul;Sanocki, Michał;Zavadlav, Julija
Learning non-local molecular interactions via equivariant local representations and charge equilibration
npj Computational Materials
2025
11
1

Fuchs, Paul;Chen, Weilong;Thaler, Stephan;Zavadlav, Julija
chemtrain-deploy: A Parallel and Scalable Framework for Machine Learning Potentials in Million-Atom MD Simulations
Journal of Chemical Theory and Computation
2025
21
15
7550-7560

Röcken, Sebastien;Zavadlav, Julija
Enhancing Machine Learning Potentials through Transfer Learning across Chemical Elements
Journal of Chemical Information and Modeling
2025

Fuchs, Paul;Thaler, Stephan;Röcken, Sebastien;Zavadlav, Julija
chemtrain: Learning deep potential models via automatic differentiation and statistical physics
Computer Physics Communications
2025
310
109512

Röcken, Sebastien;Burnet, Anton F.;Zavadlav, Julija
Predicting solvation free energies with an implicit solvent machine learning potential
The Journal of Chemical Physics
2024
161
23

Thaler, Stephan;Mayr, Felix;Thomas, Siby;Gagliardi, Alessio;Zavadlav, Julija
Active learning graph neural networks for partial charge prediction of metal-organic frameworks via dropout Monte Carlo
npj Computational Materials
2024
10
1

Röcken, Sebastien;Zavadlav, Julija
Accurate machine learning force fields via experimental and simulation data fusion
npj Computational Materials
2024
10
1

Thaler, Stephan;Fuchs, Paul;Cukarska, Ana;Zavadlav, Julija
JaxSGMC: Modular stochastic gradient MCMC in JAX
SoftwareX
2024
26
101722

Coste, Amaury;Slejko, Ema;Zavadlav, Julija;Praprotnik, Matej
Developing an Implicit Solvation Machine Learning Model for Molecular Simulations of Ionic Media
Journal of Chemical Theory and Computation
2023

Thaler, Stephan;Doehner, Gregor;Zavadlav, Julija
Scalable Bayesian Uncertainty Quantification for Neural Network Potentials: Promise and Pitfalls
Journal of Chemical Theory and Computation
2023
19
14
4520–4532