Julija Zavadlav

19 papers Journal 19

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Coarse-Grained Boltzmann Generators. CoRR Weilong Chen, Bojun Zhao, Jan Eckwert, Julija Zavadlav
2026	J	`jnl`	Mapping Still Matters: Coarse-Graining with Machine Learning Potentials. J. Chem. Inf. Model. Franz Görlich, Julija Zavadlav
2025	J	`jnl`	Enhanced Sampling for Efficient Learning of Coarse-Grained Machine Learning Potentials. CoRR Weilong Chen, Franz Görlich, Paul Fuchs, Julija Zavadlav
2025	J	`jnl`	Enhancing Machine Learning Potentials through Transfer Learning across Chemical Elements. CoRR Sebastien Röcken, Julija Zavadlav
2025	J	`jnl`	Enhancing Machine Learning Potentials through Transfer Learning across Chemical Elements. J. Chem. Inf. Model. Sebastien Röcken, Julija Zavadlav
2025	J	`jnl`	Generalization of Long-Range Machine Learning Potentials in Complex Chemical Spaces. CoRR Michal Sanocki, Julija Zavadlav
2025	J	`jnl`	Learning Non-Local Molecular Interactions via Equivariant Local Representations and Charge Equilibration. CoRR Paul Fuchs, Michal Sanocki, Julija Zavadlav
2025	J	`jnl`	Morphology-Specific Peptide Discovery via Masked Conditional Generative Modeling. CoRR Nuno Costa, Julija Zavadlav
2025	J	`jnl`	Refining Machine Learning Potentials through Thermodynamic Theory of Phase Transitions. CoRR Paul Fuchs, Julija Zavadlav
2025	J	`jnl`	chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations. CoRR Paul Fuchs, Weilong Chen, Stephan Thaler, Julija Zavadlav
2025	J	`jnl`	chemtrain: Learning deep potential models via automatic differentiation and statistical physics. Comput. Phys. Commun. Paul Fuchs, Stephan Thaler, Sebastien Röcken, Julija Zavadlav
2024	J	`jnl`	JaxSGMC: Modular stochastic gradient MCMC in JAX. SoftwareX Stephan Thaler, Paul Fuchs, Ana Cukarska, Julija Zavadlav
2024	J	`jnl`	Predicting solvation free energies with an implicit solvent machine learning potential. CoRR Sebastien Röcken, Anton F. Burnet, Julija Zavadlav
2024	J	`jnl`	chemtrain: Learning Deep Potential Models via Automatic Differentiation and Statistical Physics. CoRR Paul Fuchs, Stephan Thaler, Sebastien Röcken, Julija Zavadlav
2023	J	`jnl`	Accurate machine learning force fields via experimental and simulation data fusion. CoRR Sebastien Röcken, Julija Zavadlav
2022	J	`jnl`	Scalable Bayesian Uncertainty Quantification for Neural Network Potentials: Promise and Pitfalls. CoRR Stephan Thaler, Gregor Doehner, Julija Zavadlav
2021	J	`jnl`	Accelerated Simulations of Molecular Systems through Learning of their Effective Dynamics. CoRR Pantelis-Rafail Vlachas, Julija Zavadlav, Matej Praprotnik, Petros Koumoutsakos
2021	J	`jnl`	Learning neural network potentials from experimental data via Differentiable Trajectory Reweighting. CoRR Stephan Thaler, Julija Zavadlav
2018	J	`jnl`	Molecular Dynamics Simulation of High Density DNA Arrays. Comput. Rudolf Podgornik, Julija Zavadlav, Matej Praprotnik