Molecular Dynamics Simulation of High Density DNA Arrays. - Rudolf Podgornik; Julija Zavadlav; Matej Praprotnik (Comput., 2018)

Accelerated Simulations of Molecular Systems through Learning of their Effective Dynamics. - Pantelis-Rafail Vlachas; Julija Zavadlav; Matej Praprotnik; Petros Koumoutsakos (CoRR, 2021)

Learning neural network potentials from experimental data via Differentiable Trajectory Reweighting. - Stephan Thaler; Julija Zavadlav (CoRR, 2021)

Scalable Bayesian Uncertainty Quantification for Neural Network Potentials: Promise and Pitfalls. - Stephan Thaler; Gregor Doehner; Julija Zavadlav (CoRR, 2022)

Accurate machine learning force fields via experimental and simulation data fusion. - Sebastien Röcken; Julija Zavadlav (CoRR, 2023)

Predicting solvation free energies with an implicit solvent machine learning potential. - Sebastien Röcken; Anton F. Burnet; Julija Zavadlav (CoRR, 2024)

JaxSGMC: Modular stochastic gradient MCMC in JAX. - Stephan Thaler; Paul Fuchs; Ana Cukarska; Julija Zavadlav (SoftwareX, 2024)

chemtrain: Learning deep potential models via automatic differentiation and statistical physics. - Paul Fuchs; Stephan Thaler; Sebastien Röcken; Julija Zavadlav (Comput. Phys. Commun., 2025)

Enhancing Machine Learning Potentials through Transfer Learning across Chemical Elements. - Sebastien Röcken; Julija Zavadlav (J. Chem. Inf. Model., 2025)

Learning Non-Local Molecular Interactions via Equivariant Local Representations and Charge Equilibration. - Paul Fuchs; Michal Sanocki; Julija Zavadlav (CoRR, 2025)

chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations. - Paul Fuchs; Weilong Chen; Stephan Thaler; Julija Zavadlav (CoRR, 2025)