| Learning neural network potentials from experimental data via Differentiable Trajectory Reweighting S Thaler, J Zavadlav Nature communications 12 (1), 6884, 2021 | 48 | 2021 |
| Sparse identification of truncation errors S Thaler, L Paehler, NA Adams Journal of Computational Physics 397, 108851, 2019 | 31 | 2019 |
| Deep coarse-grained potentials via relative entropy minimization S Thaler, M Stupp, J Zavadlav The Journal of Chemical Physics 157 (24), 2022 | 11 | 2022 |
| Back-mapping augmented adaptive resolution simulation S Thaler, M Praprotnik, J Zavadlav The Journal of Chemical Physics 153 (16), 2020 | 10 | 2020 |
| Scalable Bayesian uncertainty quantification for neural network potentials: promise and pitfalls S Thaler, G Doehner, J Zavadlav Journal of Chemical Theory and Computation 19 (14), 4520-4532, 2023 | 7 | 2023 |
| Uncertainty Quantification for Molecular Models via Stochastic Gradient MCMC S Thaler, J Zavadlav 10th Vienna Conference on Mathematical Modelling, 19-20, 2022 | 2 | 2022 |
| JaxSGMC: Modular stochastic gradient MCMC in JAX S Thaler, P Fuchs, A Cukarska, J Zavadlav SoftwareX 26, 101722, 2024 | | 2024 |
| Data Driven Modeling of the Laminar Flame Response using Universal Differential Equations G Doehner, CF Silva, S Thaler, J Zavadlav, W Polifke | | 2023 |
| Advances in Neural Network Potentials for Molecular Dynamics Simulations: Physics-Informed Training and Uncertainty Quantification S Thaler Technische Universität München, 2023 | | 2023 |
| Partial Charge Assignment to Metal Organic Frameworks through Active Learning S Thaler, F Mayr, S Thomas, A Gagliardi, J Zavadlav ARTEMIS 1st Plenary meeting, 2022 | | 2022 |
Julija ZavadlavTechnical University of MunichVerified email at tum.de
