Fast, Modular, and Differentiable Framework for Machine Learning-Enhanced Molecular Simulations

Henrik Christiansen, Takashi Maruyama, Federico Errica +3

We present an end-to-end differentiable molecular simulation framework (DIMOS) for molecular dynamics and Monte Carlo simulations. DIMOS easily integrates machine-learning-based in…