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TorchMD – A Deep Learning Framework For Molecular Simulations

New MD code available
TorchMD – A Deep Learning Framework For Molecular Simulations
Published on
March 17, 2021

This week our paper on TorchMD has been published in the Journal of Chemical Theory and Computation. TorchMD is a framework for molecular simulations that enables users to do research faster in force-field development as well as integrate neural network potentials seamlessly into the dynamics with the simplicity and power of PyTorch. It is the result of the collaboration of Acellera with Universitat Pompeu Fabra, Freie Universität Berlin, Rice University, CNR-IBF- Italy, Università degli Studi di Milano and ICREA.

Molecular dynamics simulations provide a mechanistic description of molecules by relying on empirical potentials. The quality and transferability of such potentials can be improved by leveraging data-driven models derived with machine learning approaches. With TorchMD, we aim to provide the glue which connects classical molecular simulations with machine learning methods. All force computations including bond, angle, dihedral, Lennard-Jones and Coulomb interactions are expressed as PyTorch arrays and operations. It also enables learning and simulating with neural network potentials. TorchMD has been validated using standard Amber all-atom simulations, learning an ab-initio potential, performing an end-to-end training and finally, learning and simulating a coarse grained model for protein folding.

What Will Come Next?

The end-to-end differentiability of parameters, which TorchMD offers, is a feature that projects such as the Open Force Field Initiative can already start exploiting. For faster, production-ready simulations, we are working on facilitating the integration of machine learning potentials in OpenMM and ACEMD3.

Meanwhile, we believe that TorchMD can play an important role by facilitating experimentation between ML and MD fields, speeding up the model train-evaluate prototyping cycle, and promoting the adoption of data-based approaches in molecular simulations. All the code used to produce and evaluate the models is available for practitioners at github.com/torchmd. We invite you to use it and drop us a line on your experience.

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