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A GPU-Accelerated Machine Learning Framework for Molecular Simulation: HOOMD-blue with TensorFlow

revised on 14.10.2019, 17:37 and posted on 16.10.2019, 07:19 by Rainier Barrett, Maghesree Chakraborty, Dilnoza Amirkulova, Heta Gandhi, Andrew White

As interest grows in applying machine learning force-fields and methods to molecular simulation, there is a need for state-of-the-art inference methods to use trained models within efficient molecular simulation engines. We have designed and implemented software that enables integration of a scalable GPU-accelerated molecular mechanics engine, HOOMD-blue, with the machine learning (ML) TensorFlow package. TensorFlow is a GPU-accelerated, scalable, graph-based tensor computation model building package that has been the implementation of many recent innovations in deep learning and other ML tasks. TensorFlow models are constructed in Python and can be visualized or debugged using the rich set of tools implemented in the TensorFlow package. In this article, we present four major examples of tasks this software can accomplish which would normally require multiple different tools: (1) we train a neural network to reproduce a force field of a Lennard-Jones simulation; (2) we perform online force matching of methanol; (3) we compute the maximum entropy bias of a Lennard-Jones collective variable; (4) we calculate the scattering profile of an ongoing TIP4P water molecular dynamics simulation. This work should accelerate both the design of new neural network based models in computational chemistry research and reproducible model specification by leveraging a widely-used ML package.


CAREER: Multiscale Modeling of Peptide Self-Assembly with Experiment Directed Simulation

Directorate for Engineering

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CDS&E: D3SC: Applying Video Segmentation to Coarse-grain Mapping Operators in Molecular Simulations

Directorate for Mathematical & Physical Sciences

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University of Rochester


United States

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Declaration of Conflict of Interest

No conflict of interest