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

Thaler, Stephan;Zavadlav, Julija
Uncertainty Quantification for Molecular Models via Stochastic Gradient MCMC
MATHMOD 2022 Discussion Contribution Volume
ARGESIM Publisher Vienna
2022

Thaler, Stephan;Stupp, Maximilian;Zavadlav, Julija
Deep Coarse-grained Potentials via Relative Entropy Minimization
The Journal of Chemical Physics
2022
157
244103

Zavadlav, Julija;Melo, Manuel Nuno;Marrink, Siewert J.;Praprotnik, Matej
Adaptive resolution simulation of an atomistic protein in MARTINI water
The Journal of Chemical Physics
2014
140
5
054114

Zavadlav, Julija;Melo, Manuel N.;Cunha, Ana V.;de Vries, Alex H.;Marrink, Siewert J.;Praprotnik, Matej
Adaptive Resolution Simulation of MARTINI Solvents
Journal of Chemical Theory and Computation
2014
10
6
2591-2598