6 papers
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…
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, MichaÅ 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-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…
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…