112 citations · 237 across the 19 of their papers we have counts for
13 papers · 1 filter
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…
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,…
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…
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…