112 citations · 237 across the 19 of their papers we have counts for
22 papers
SupraTITO: Transferable Generative Molecular Dynamics for Supramolecular Systems
Weilong Chen, Nuno Costa, Julija Zavadlav
Peptide sequence governs both the structures formed through supramolecular assembly and the dynamics by which they emerge, but predicting either requires resolving slow collective…
Transferable Implicit Solvent Machine Learning Potential for Drugs and Proteins Approaching Ab Initio Accuracy
Jan Eckwert, Julija Zavadlav
Machine learning interatomic potentials (MLPs) have revolutionized atomistic modeling, offering the potential to replace traditional methods like Density Functional Theory (DFT). H…
ConSolv: Solvent-Conditional Machine Learning Implicit Solvent Potential
Linying Zhang, Julija Zavadlav
Implicit solvent machine learning potentials (MLPs) offer a powerful route to bridging the gap between accuracy and efficiency in molecular simulations. However, existing models ha…
Aluminum solidification and nanopolycrystal deformation via a Graph Neural Network Potential and Million-Atom Simulations
Ian Störmer, Julija Zavadlav
Solidification governs the microstructure and, therefore, the mechanical response of metal components, yet the atomistic details of nucleation and defect formation are often diffic…
Coarse-Grained Boltzmann Generators
Weilong Chen, Bojun Zhao, Jan Eckwert +1
Sampling equilibrium molecular configurations from the Boltzmann distribution is a longstanding challenge. Boltzmann Generators (BGs) address this by combining exact-likelihood gen…
Mapping Still Matters: Coarse-Graining with Machine Learning Potentials
Franz Görlich, Julija Zavadlav
Coarse-grained (CG) modeling enables molecular simulations to reach time and length scales inaccessible to fully atomistic methods. For classical CG models, the choice of mapping,…
Refining Machine Learning Potentials through Thermodynamic Theory of Phase Transitions
Paul Fuchs, Julija Zavadlav
Foundational Machine Learning Potentials can resolve the accuracy and transferability limitations of classical force fields. They enable microscopic insights into material behavior…
Generalization of Long-Range Machine Learning Potentials in Complex Chemical Spaces
Michal Sanocki, Julija Zavadlav
The vastness of chemical space makes generalization a central challenge in the development of machine learning interatomic potentials (MLIPs). While MLIPs could enable large-scale…
Enhanced Sampling for Efficient Learning of Coarse-Grained Machine Learning Potentials
Weilong Chen, Franz Görlich, Paul Fuchs +1
Coarse-graining (CG) enables molecular dynamics (MD) simulations of larger systems and longer timescales that are otherwise infeasible with atomistic models. Machine learning poten…
Morphology-Aware Peptide Discovery via Masked Conditional Generative Modeling
Nuno Costa, Julija Zavadlav
Peptide self-assembly prediction offers a powerful bottom-up strategy for designing biocompatible, low-toxicity materials for large-scale synthesis in a broad range of biomedical a…
chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations
Paul Fuchs, Weilong Chen, Stephan Thaler +1
Machine learning potentials (MLPs) have advanced rapidly and show great promise to transform molecular dynamics (MD) simulations. However, most existing software tools are tied to…
JaxSGMC: Modular stochastic gradient MCMC in JAX
Stephan Thaler, Paul Fuchs, Ana Cukarska +1
We present JaxSGMC, an application-agnostic library for stochastic gradient Markov chain Monte Carlo (SG-MCMC) in JAX. SG-MCMC schemes are uncertainty quantification (UQ) methods t…
Enhancing Machine Learning Potentials through Transfer Learning across Chemical Elements
Sebastien Röcken, Julija Zavadlav
Machine Learning Potentials (MLPs) can enable simulations of ab initio accuracy at orders of magnitude lower computational cost. However, their effectiveness hinges on the availabi…
Learning Non-Local Molecular Interactions via Equivariant Local Representations and Charge Equilibration
Paul Fuchs, Michał Sanocki, Julija Zavadlav
Graph Neural Network (GNN) potentials relying on chemical locality offer near-quantum mechanical accuracy at significantly reduced computational costs. Message-passing GNNs model i…
chemtrain: Learning Deep Potential Models via Automatic Differentiation and Statistical Physics
Paul Fuchs, Stephan Thaler, Sebastien Röcken +1
Neural Networks (NNs) are effective models for refining the accuracy of molecular dynamics, opening up new fields of application. Typically trained bottom-up, atomistic NN potentia…
Predicting solvation free energies with an implicit solvent machine learning potential
Sebastien Röcken, Anton F. Burnet, Julija Zavadlav
Machine learning (ML) potentials are a powerful tool in molecular modeling, enabling ab initio accuracy for comparably small computational costs. Nevertheless, all-atom simulations…
Accurate machine learning force fields via experimental and simulation data fusion
Sebastien Röcken, Julija Zavadlav
Machine Learning (ML)-based force fields are attracting ever-increasing interest due to their capacity to span spatiotemporal scales of classical interatomic potentials at quantum-…
Scalable Bayesian Uncertainty Quantification for Neural Network Potentials: Promise and Pitfalls
Stephan Thaler, Gregor Doehner, Julija Zavadlav
Neural network (NN) potentials promise highly accurate molecular dynamics (MD) simulations within the computational complexity of classical MD force fields. However, when applied o…
Deep Coarse-grained Potentials via Relative Entropy Minimization
Stephan Thaler, Maximilian Stupp, Julija Zavadlav
Neural network (NN) potentials are a natural choice for coarse-grained (CG) models. Their many-body capacity allows highly accurate approximations of the potential of mean force, p…
Learning neural network potentials from experimental data via Differentiable Trajectory Reweighting
Stephan Thaler, Julija Zavadlav
In molecular dynamics (MD), neural network (NN) potentials trained bottom-up on quantum mechanical data have seen tremendous success recently. Top-down approaches that learn NN pot…
Accelerated Simulations of Molecular Systems through Learning of their Effective Dynamics
Pantelis R. Vlachas, Julija Zavadlav, Matej Praprotnik +1
Simulations are vital for understanding and predicting the evolution of complex molecular systems. However, despite advances in algorithms and special purpose hardware, accessing t…
Bayesian selection for coarse-grained models of liquid water
Julija Zavadlav, Georgios Arampatzis, Petros Koumoutsakos
The necessity for accurate and computationally efficient representations of water in atomistic simulations that can span biologically relevant timescales has born the necessity of…