@inproceedings{
dobers2023latent,
title={Latent Space Simulator for Unveiling Molecular Free Energy Landscapes and Predicting Transition Dynamics},
author={Simon Dobers and Hannes Stark and Xiang Fu and Dominique Beaini and Stephan G{\"u}nnemann},
booktitle={NeurIPS 2023 AI for Science Workshop},
year={2023},
url={https://openreview.net/forum?id=XIxcglPy9c}
}
Free Energy Surfaces (FES) and metastable transition rates are key elements in understanding the behavior of molecules within a system. However, the typical approaches require computing force fields across billions of time steps in a molecular dynamics (MD) simulation, which is often considered intractable when dealing with large systems or databases. In this work, we propose LaMoDy, a latent-space MD simulator, to effectively tackle the intractability with around 20-fold speed improvements compared to classical MD. The model leverages a chirality-aware SE(3)-invariant encoder-decoder architecture to generate a latent space coupled with a recurrent neural network to run the time-wise dynamics. We show that LaMoDy effectively recovers realistic trajectories and FES more accurately and faster than existing methods while capturing their major dynamical and conformational properties. Furthermore, the proposed approach can generalize to molecules outside the training distribution.
Latent Space Simulator for Unveiling Molecular Free Energy Landscapes and Predicting Transition Dynamics
Simon Dobers Hannes Stark Xiang Fu Dominique Beaini Stephan Günnemann