Center for Computing Research (CCR)

Center for Computing Research

Computational Multiscale, 01444

The Computational Multiscale Department performs research and development in physics-based materials modeling and high-performance computing.  We combine expertise across multiple disciplines to solve science and engineering problems in support of the DOE mission.  Our team brings together experts in density functional theory, molecular dynamics, direct simulation Monte Carlo, kinetic Monte Carlo, microstructure modeling, continuum mechanics, equations of state, and peridynamics.  We lead a number of software development efforts, including the LAMMPS molecular dynamics code, and strive to advance the state of the art in materials modeling through a broad range of collaborations across the laboratories.

People

David John Littlewood
Manager, Computational Multiscale
Email: djlittl@sandia.gov
Phone: 505/284-0830
Fax: 505/845-7442

Mailing address:
Sandia National Laboratories
P.O. Box 5800, MS 1322
Albuquerque, NM
87185-1320
Lisa Mahkee, Office Administrative Assistant
Staff
John H. Carpenter
Daniel Spencer Jensen
John A. Mitchell
Stan Gerald Moore
Svetoslav Valeriev Nikolov
Steven J. Plimpton
Joshua Rackers (Josh)
Joshua Robbins
Peter A. Schultz
Charles Andrew Sievers
Stewart A. Silling
Aidan P. Thompson
Jeremy Trageser
Julien Guy Tranchida
Mitchell Wood

News

  • Machine-Learned Interatomic Potentials Are Now Plug-And-Play in LAMMPS

    Researchers at Sandia and Los Alamos National Laboratories have discovered a new way to implement machine learning (ML) interatomic potentials in the LAMMPS molecular dynamics code. This makes it much easier to prototype and deploy ML models in LAMMPS and provides access to a vast reservoir of existing ML libraries....

    Machine-Learned Interatomic Potentials Are Now Plug-And-Play in LAMMPS

    Researchers at Sandia and Los Alamos National Laboratories have discovered a new way to implement machine learning (ML) interatomic potentials in the LAMMPS molecular dynamics code. This makes it much easier to prototype and deploy ML models in LAMMPS and provides access to a vast reservoir of existing ML libraries.  The key is to define an interface (MLIAP) that separates the calculation of atomic fingerprints from the prediction of energy.  The interface also separates the atomic descriptors and energy models from the specialized LAMMPS data structures needed for efficient simulations on massively parallel computers. Most recently, a new model class has been added to MLIAP that provides access to any Python-based library, including the PyTorch neural network framework.  This advancement was made under the DOE SciDAC/FES FusMatML project for the application of machine learning to atomistic models for plasma-facing materials in fusion reactors.

     

    More information on the LAMMPS package for machine-learned interatomic potentials can be found here: LAMMPS MLIAP Package

     

    Contact: Thompson, Aidan P.
    January 2021
    1255798

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