Emerging Trends in Molecular Dynamics Simulations and Machine Learning III

Presentations

• The Self Learning Kinetic Monte Carlo (SLKMC) method augmented with data analytics for adatom-island diffusion on surfaces

  Invited

  March 4, 2020, 1:15 PM – March 4, 2020, 1:51 PM

  Presenters

  • Talat Rahman

    Department of Physics, University of Central Florida, Physics, Univ of Central Florida, University of Central Florida, Physics and Renewable Energy and Chemical Transformations Cluster, University of Central Florida

  Authors

  • Talat Rahman

    Department of Physics, University of Central Florida, Physics, Univ of Central Florida, University of Central Florida, Physics and Renewable Energy and Chemical Transformations Cluster, University of Central Florida

  View abstract →

• Accelerated Discovery of Dielectric Polymer Materials Using Graph Convolutional Neural Networks

  ORAL

  March 4, 2020, 1:51 PM – March 4, 2020, 2:03 PM

  Presenters

  • Ankit Mishra

    Mork Family Department of Chemical Engineering and Materials Science, University of Southern California

  Authors

  • Ankit Mishra

    Mork Family Department of Chemical Engineering and Materials Science, University of Southern California

  • Pankaj Rajak

    Argonne National Lab, LCF, Argonne National Laboratory

  • Ekin Dogus Cubuk

    Google, Google Inc., Google Inc, Google Brain

  • Ken-ichi Nomura

    Mork Family Department of Chemical Engineering and Materials Science, University of Southern California, University of Southern California, Univ of Southern California

  • Rajiv Kalia

    Mork Family Department of Chemical Engineering and Materials Science, University of Southern California, Univ of Southern California, Collaboratory for Advanced Computing and Simulations, University of Southern California

  • Aiichiro Nakano

    Mork Family Department of Chemical Engineering and Materials Science, University of Southern California, Univ of Southern California, Collaboratory for Advanced Computing and Simulations, University of Southern California

  • Ajinkya Deshmukh

    Department of Chemistry, University of Connecticut, Storrs

  • Lihua Chen

    Department of Material Science and Technology, Georgia Tech, Materials Science and Engineering, Georgia Institute of Technology

  • Greg Sotzing

    Department of Chemistry, University of Connecticut, Storrs

  • Yang Cao

    Department of Electrical Engineering, University of Connecticut, Storrs

  • Ramamurthy Ramprasad

    Georgia Institute of Technology, School of Materials Science and Engineering, Georgia Institute of Technology, Department of Material Science and Technology, Georgia Tech, Materials Science and Engineering, Georgia Institute of Technology

  • Priya Vashishta

    Mork Family Department of Chemical Engineering and Materials Science, University of Southern California, Univ of Southern California, University of Southern California, Collaboratory for Advanced Computing and Simulations, University of Southern California

  View abstract →

• Deep Learning embedding layers for better prediction of atomic forces in solids

  ORAL

  March 4, 2020, 2:03 PM – March 4, 2020, 2:15 PM

  Presenters

  • Sivan Niv

    Department of Physical Electronics, Tel Aviv University

  Authors

  • Sivan Niv

    Department of Physical Electronics, Tel Aviv University

  • Goren Gordon

    Industrial Engineering, Tel-Aviv University

  • Amir Natan

    Department of Physical Electronics, Tel Aviv University, Tel Aviv University

  View abstract →

• A molecular dynamics study of water crystallization using deep neural network potentials of ab-initio quality

  ORAL

  March 4, 2020, 2:15 PM – March 4, 2020, 2:27 PM

  Presenters

  • Pablo Piaggi

    Princeton University

  Authors

  • Pablo Piaggi

    Princeton University

  • Roberto Car

    Princeton University

  View abstract →

• Machine learning force field using decomposed atomic energies from ab initio calculations

  ORAL

  March 4, 2020, 2:27 PM – March 4, 2020, 2:39 PM

  Presenters

  • Lin-Wang Wang

    Materials Science Division, Lawrence Berkeley National Laboratory, Lawrence Berkeley National Laboratory

  Authors

  • Lin-Wang Wang

    Materials Science Division, Lawrence Berkeley National Laboratory, Lawrence Berkeley National Laboratory

  View abstract →

• Machine learning to derive quantum-informed and chemically-aware force fields to simulate interfaces and defects in hybrid halide perovskites

  ORAL

  March 4, 2020, 2:39 PM – March 4, 2020, 2:51 PM

  Presenters

  • Ross E Larsen

    National Renewable Energy Laboratory

  Authors

  • Ross E Larsen

    National Renewable Energy Laboratory

  • Matthew Jankousky

    National Renewable Energy Laboratory

  • Derek Vigil-Fowler

    National Renewable Energy Laboratory

  • Aaron M Holder

    National Renewable Energy Laboratory

  • K. Grace Johnson

    Department of Chemistry, Stanford University

  View abstract →

• Active Learning of Coarse Grained Force Fields with Gaussian Process Regression

  ORAL

  March 4, 2020, 2:51 PM – March 4, 2020, 3:03 PM

  Presenters

  • Blake Duschatko

    Harvard University

  Authors

  • Blake Duschatko

    Harvard University

  • Jonathan Vandermause

    Harvard University, School of Engineering and Applied Science, Harvard University

  • Nicola Molinari

    Harvard University, School of Engineering and Applied Sciences, Harvard University

  • Boris Kozinsky

    Harvard University, School of Engineering and Applied Sciences, Harvard University, School of Engineering and Applied Science, Harvard University

  View abstract →

• External Potential Ensembles to Improve the Learning of Transferable Coarse-Grained Potentials

  ORAL

  March 4, 2020, 3:03 PM – March 4, 2020, 3:15 PM

  Presenters

  • Kevin Shen

    University of California, Santa Barbara

  Authors

  • Kevin Shen

    University of California, Santa Barbara

  • Kris T Delaney

    University of California, Santa Barbara

  • M. Scott Shell

    University of California, Santa Barbara

  • Glenn H Fredrickson

    University of California, Santa Barbara, Chemical Engineering, University of California, Santa Barbara

  View abstract →

• Data-driven parameterization of coarse-grained models of soft materials using machine learning tools

  ORAL

  March 4, 2020, 3:15 PM – March 4, 2020, 3:27 PM

  Presenters

  • Lilian Johnson

    National Institute of Standards and Technology

  Authors

  • Lilian Johnson

    National Institute of Standards and Technology

  • Frederick Phelan

    National Institute of Standards and Technology

  View abstract →

• JAX, M.D. End-to-End Differentiable, Hardware Accelerated, Molecular Dynamics in Pure Python

  ORAL

  March 4, 2020, 3:27 PM – March 4, 2020, 3:39 PM

  Presenters

  • Sam Schoenholz

    Google, Google Inc., Google Brain

  Authors

  • Sam Schoenholz

    Google, Google Inc., Google Brain

  • Ekin Dogus Cubuk

    Google, Google Inc., Google Inc, Google Brain

  View abstract →

• A neural network interatomic potential for molten NaCl

  ORAL

  March 4, 2020, 3:39 PM – March 4, 2020, 3:51 PM

  Presenters

  • Qingjie Li

    Massachusetts Institute of Technology MIT

  Authors

  • Qingjie Li

    Massachusetts Institute of Technology MIT

  • Emine Kucukbenli

    Harvard University

  • Stephen Lam

    Massachusetts Institute of Technology MIT

  • Boris Khaykovich

    Massachusetts Institute of Technology MIT

  • Efthimios Kaxiras

    Harvard University, Department of Physics, Harvard University

  • Ju Li

    Massachusetts Institute of Technology MIT

  View abstract →

• Simulating Aluminum Corrosion Using DFT Trained Deep Neural Network Potentials

  ORAL

  March 4, 2020, 3:51 PM – March 4, 2020, 4:03 PM

  Presenters

  • Wissam A Saidi

    Mechanical Engineering & Materials Science, University of Pittsburg, Univ of Pittsburgh, Department of Materials Science and Engineering, University of Pittsburgh

  Authors

  • Wissam A Saidi

    Mechanical Engineering & Materials Science, University of Pittsburg, Univ of Pittsburgh, Department of Materials Science and Engineering, University of Pittsburgh

  • Shyam Dwaraknath

    Lawrence Berkeley National Laboratory, Energy Technologies Area, Lawrence Berkeley National Laboratory

  View abstract →

• Tensor-Field Molecular Dynamics: A Deep Learning model for highly accurate, symmetry-preserving force-fields from small data sets

  ORAL

  March 4, 2020, 4:03 PM – March 4, 2020, 4:15 PM

  Presenters

  • Simon Batzner

    Harvard University, School of Engineering and Applied Science, Harvard University

  Authors

  • Simon Batzner

    Harvard University, School of Engineering and Applied Science, Harvard University

  • Lixin Sun

    Harvard University, School of Engineering and Applied Science, Harvard University

  • Tess E Smidt

    Lawrence Berkeley National Laboratory, Computational Research Division, Lawrence Berkeley National Laboratory

  • Boris Kozinsky

    Harvard University, School of Engineering and Applied Sciences, Harvard University, School of Engineering and Applied Science, Harvard University

  View abstract →