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