Sparse identification of truncation errors. - Stephan Thaler; Ludger Paehler; Nikolaus A. Adams (J. Comput. Phys., 2019)

Learning neural network potentials from experimental data via Differentiable Trajectory Reweighting. - Stephan Thaler; Julija Zavadlav (CoRR, 2021)

Scalable Bayesian Uncertainty Quantification for Neural Network Potentials: Promise and Pitfalls. - Stephan Thaler; Gregor Doehner; Julija Zavadlav (CoRR, 2022)

ET-Flow: Equivariant Flow-Matching for Molecular Conformer Generation. - Majdi Hassan; Nikhil Shenoy; Jungyoon Lee; Hannes Stärk; Stephan Thaler; Dominique Beaini (NeurIPS, 2024)

OpenQDC: Open Quantum Data Commons. - Cristian Gabellini; Nikhil Shenoy; Stephan Thaler; Semih Cantürk; Daniel McNeela; Dominique Beaini; Michael M. Bronstein; Prudencio Tossou (CoRR, 2024)

Implicit Delta Learning of High Fidelity Neural Network Potentials. - Stephan Thaler; Cristian Gabellini; Nikhil Shenoy; Prudencio Tossou (CoRR, 2024)

JaxSGMC: Modular stochastic gradient MCMC in JAX. - Stephan Thaler; Paul Fuchs; Ana Cukarska; Julija Zavadlav (SoftwareX, 2024)

chemtrain: Learning deep potential models via automatic differentiation and statistical physics. - Paul Fuchs; Stephan Thaler; Sebastien Röcken; Julija Zavadlav (Comput. Phys. Commun., 2025)

chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations. - Paul Fuchs; Weilong Chen; Stephan Thaler; Julija Zavadlav (CoRR, 2025)