Title
Energy-Based Coarse-Graining in Molecular Dynamics: A Flow-Based Framework Without Data Maximilian Stupp P. S. Koutsourelakis 40 0 0 29 Apr 2025
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Scalable Bayesian Uncertainty Quantification for Neural Network Potentials: Promise and Pitfalls Stephan Thaler Gregor Doehner J. Zavadlav 35 21 0 15 Dec 2022
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Developing Machine-Learned Potentials for Coarse-Grained Molecular Simulations: Challenges and Pitfalls E. Ricci George Giannakopoulos V. Karkaletsis D. Theodorou Niki Vergadou AI4CE 25 9 0 26 Sep 2022
Graph neural networks for materials science and chemistry Patrick Reiser Marlen Neubert André Eberhard Luca Torresi Chen Zhou ... Houssam Metni Clint van Hoesel Henrik Schopmans T. Sommer Pascal Friederich GNN AI4CE 45 373 0 05 Aug 2022
TorchMD-NET: Equivariant Transformers for Neural Network based Molecular Potentials Philipp Thölke Gianni de Fabritiis AI4CE 34 185 0 05 Feb 2022
GraphVAMPNet, using graph neural networks and variational approach to markov processes for dynamical modeling of biomolecules Mahdi Ghorbani Samarjeet Prasad Jeffery B. Klauda B. Brooks GNN 22 30 0 12 Jan 2022
Graph Neural Networks Accelerated Molecular Dynamics Zijie Li Kazem Meidani Prakarsh Yadav A. Farimani GNN AI4CE 18 53 0 06 Dec 2021
Learning neural network potentials from experimental data via Differentiable Trajectory Reweighting Stephan Thaler J. Zavadlav 20 66 0 02 Jun 2021
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Relevance of Rotationally Equivariant Convolutions for Predicting Molecular Properties Benjamin Kurt Miller Mario Geiger Tess E. Smidt Frank Noé 16 75 0 19 Aug 2020
Variational Koopman models: slow collective variables and molecular kinetics from short off-equilibrium simulations Hao Wu Feliks Nuske Fabian Paul Stefan Klus P. Koltai Frank Noé 107 126 0 20 Oct 2016
Estimation and uncertainty of reversible Markov models Benjamin Trendelkamp-Schroer Hao Wu Fabian Paul Frank Noé 68 129 0 19 Jul 2015