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TorchMD: A deep learning framework for molecular simulations
Stefan Doerr, Maciej Majewsk, Adrià Pérez +5
Molecular dynamics simulations provide a mechanistic description of molecules by relying on empirical potentials. The quality and transferability of such potentials can be improved…
Training Invertible Linear Layers through Rank-One Perturbations
Andreas Krämer, Jonas Köhler, Frank Noé
Many types of neural network layers rely on matrix properties such as invertibility or orthogonality. Retaining such properties during optimization with gradient-based stochastic o…
Convergence to the fixed-node limit in deep variational Monte Carlo
Zeno Schätzle, Jan Hermann, Frank Noé
Variational quantum Monte Carlo (QMC) is an ab-initio method for solving the electronic Schrödinger equation that is exact in principle, but limited by the flexibility of the avail…
Coarse Graining Molecular Dynamics with Graph Neural Networks
Brooke E. Husic, Nicholas E. Charron, Dominik Lemm +9
Coarse graining enables the investigation of molecular dynamics for larger systems and at longer timescales than is possible at atomic resolution. However, a coarse graining model…
Equivariant Flows: Exact Likelihood Generative Learning for Symmetric Densities
Jonas Köhler, Leon Klein, Frank Noé
Normalizing flows are exact-likelihood generative neural networks which approximately transform samples from a simple prior distribution to samples of the probability distribution…
Ensemble Learning of Coarse-Grained Molecular Dynamics Force Fields with a Kernel Approach
Jiang Wang, Stefan Chmiela, Klaus-Robert Müller +2
Gradient-domain machine learning (GDML) is an accurate and efficient approach to learn a molecular potential and associated force field based on the kernel ridge regression algorit…