Julija Zavadlav

According to our database, Julija Zavadlav authored at least 17 papers between 2018 and 2026.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2026

Coarse-Grained Boltzmann Generators.

Weilong Chen, Bojun Zhao, Jan Eckwert, Julija Zavadlav

CoRR, February, 2026

Mapping Still Matters: Coarse-Graining with Machine Learning Potentials.

Franz Görlich, Julija Zavadlav

J. Chem. Inf. Model., 2026

2025

Generalization of Long-Range Machine Learning Potentials in Complex Chemical Spaces.

Michal Sanocki, Julija Zavadlav

CoRR, December, 2025

Refining Machine Learning Potentials through Thermodynamic Theory of Phase Transitions.

Paul Fuchs, Julija Zavadlav

CoRR, December, 2025

Enhanced Sampling for Efficient Learning of Coarse-Grained Machine Learning Potentials.

Weilong Chen, Franz Görlich, Paul Fuchs, Julija Zavadlav

CoRR, October, 2025

Morphology-Specific Peptide Discovery via Masked Conditional Generative Modeling.

Nuno Costa, Julija Zavadlav

CoRR, September, 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, June, 2025

Learning Non-Local Molecular Interactions via Equivariant Local Representations and Charge Equilibration.

Paul Fuchs, Michal Sanocki, Julija Zavadlav

CoRR, January, 2025

Enhancing Machine Learning Potentials through Transfer Learning across Chemical Elements.

Sebastien Röcken, Julija Zavadlav

J. Chem. Inf. Model., 2025

chemtrain: Learning deep potential models via automatic differentiation and statistical physics.

Paul Fuchs, Stephan Thaler, Sebastien Röcken, Julija Zavadlav

Comput. Phys. Commun., 2025

2024

JaxSGMC: Modular stochastic gradient MCMC in JAX.

Stephan Thaler, Paul Fuchs, Ana Cukarska, Julija Zavadlav

SoftwareX, 2024

Predicting solvation free energies with an implicit solvent machine learning potential.

Sebastien Röcken, Anton F. Burnet, Julija Zavadlav

CoRR, 2024

2023

Accurate machine learning force fields via experimental and simulation data fusion.

Sebastien Röcken, Julija Zavadlav

CoRR, 2023

2022

Scalable Bayesian Uncertainty Quantification for Neural Network Potentials: Promise and Pitfalls.

Stephan Thaler, Gregor Doehner, Julija Zavadlav

CoRR, 2022

2021

Learning neural network potentials from experimental data via Differentiable Trajectory Reweighting.

Stephan Thaler, Julija Zavadlav

CoRR, 2021

Accelerated Simulations of Molecular Systems through Learning of their Effective Dynamics.

Pantelis-Rafail Vlachas, Julija Zavadlav, Matej Praprotnik, Petros Koumoutsakos

CoRR, 2021

2018

Molecular Dynamics Simulation of High Density DNA Arrays.

Rudolf Podgornik, Julija Zavadlav, Matej Praprotnik

Comput., 2018