Emerging Trends in Molecular Dynamics Simulations and Machine Learning IV

Presentations

• Using Topological Constraints to Modify Polymer Materials

  Invited

  March 4, 2020, 2:30 PM – March 4, 2020, 3:06 PM

  Presenters

  • Kurt Kremer

    • Max Planck Inst
    • Max Planck Institute for Polymer Research

  Authors

  • Kurt Kremer

    • Max Planck Inst
    • Max Planck Institute for Polymer Research

  View abstract →

• Simpler is Better: How Linear Prediction Tasks Improve Transfer Learning in Chemical Autoencoders

  ORAL

  March 4, 2020, 3:06 PM – March 4, 2020, 3:18 PM

  Presenters

  • Nick Iovanac

    • Purdue Univ

  Authors

  • Nick Iovanac

    • Purdue Univ

  • Brett Savoie

    • Purdue Univ

  View abstract →

• Neural Network Based Molecular Dynamics to Study Polymers

  ORAL

  March 4, 2020, 3:18 PM – March 4, 2020, 3:30 PM

  Presenters

  • Christopher Kuenneth

    • School of Materials Science and Engineering, Georgia Institute of Technology

  Authors

  • Christopher Kuenneth

    • School of Materials Science and Engineering, Georgia Institute of Technology

  • 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

  View abstract →

• Applications of Automatic Differentiation to Materials Design

  ORAL

  March 4, 2020, 3:30 PM – March 4, 2020, 3:42 PM

  Presenters

  • Ella King

    • Harvard University

  Authors

  • Ella King

    • Harvard University

  • Carl Goodrich

    • Harvard University

  • Sam Schoenholz

    • Google
    • Google Inc.
    • Google Brain

  • Ekin Dogus Cubuk

    • Google
    • Google Inc.
    • Google Inc
    • Google Brain

  • Michael Phillip Brenner

    • Harvard University

  View abstract →

• Trainable Molecular Dynamics Models

  ORAL

  March 4, 2020, 3:42 PM – March 4, 2020, 3:54 PM

  Presenters

  • Carl Goodrich

    • Harvard University

  Authors

  • Carl Goodrich

    • Harvard University

  • Ella King

    • Harvard University

  • Samuel Schoenholz

    • Google Brain
    • Google

  • Ekin Dogus Cubuk

    • Google
    • Google Inc.
    • Google Inc
    • Google Brain

  • Michael Phillip Brenner

    • Harvard University

  View abstract →

• Hydrogen-Oxygen Combustion: Data-Driven Generation of Quantum-Accurate Interatomic Potentials

  ORAL

  March 4, 2020, 3:54 PM – March 4, 2020, 4:06 PM

  Presenters

  • Allan Avila

    • University of California, Santa Barbara

  Authors

  • Allan Avila

    • University of California, Santa Barbara

  • Luke Bertels

    • University of California, Berkeley

  • Igor Mezic

    • University of California, Santa Barbara

  • Martin P Head-Gordon

    • University of California, Berkeley

  View abstract →

• Toward optimal descriptors for accurate machine learning of flexible molecules

  ORAL

  March 4, 2020, 4:06 PM – March 4, 2020, 4:18 PM

  Presenters

  • Valentin Vassilev Galindo

    • Physics and Materials Science Research Unit, University of Luxembourg
    • University of Luxembourg Limpertsberg

  Authors

  • Valentin Vassilev Galindo

    • Physics and Materials Science Research Unit, University of Luxembourg
    • University of Luxembourg Limpertsberg

  • Igor Poltavskyi

    • University of Luxembourg Limpertsberg
    • University of Luxembourg

  • Alexandre Tkatchenko

    • University of Luxembourg Limpertsberg
    • Physics and Materials Science Research Unit, University of Luxembourg
    • University of Luxembourg

  View abstract →

• Towards transferable parametrization of Density-Functional Tight-Binding with machine learning

  ORAL

  March 4, 2020, 4:18 PM – March 4, 2020, 4:30 PM

  Presenters

  • Leonardo Medrano Sandonas

    • Physics and Materials Science Reasearch Unit, University of Luxembourg

  Authors

  • Leonardo Medrano Sandonas

    • Physics and Materials Science Reasearch Unit, University of Luxembourg

  • Martin Stoehr

    • Physics and Materials Science Reasearch Unit, University of Luxembourg
    • Physics and Materials Science Research Unit, University of Luxembourg

  • Alexandre Tkatchenko

    • Physics and Materials Science Reasearch Unit, University of Luxembourg
    • Physics and Materials Science Research Unit, University of Luxembourg
    • University of Luxembourg
    • University of Luxembourg Limpertsberg

  View abstract →

• Active learning of fast Bayesian force fields with mapped gaussian processes - application to stability of stanene

  ORAL

  March 4, 2020, 4:30 PM – March 4, 2020, 4:42 PM

  Presenters

  • Yu Xie

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

  Authors

  • Yu Xie

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

  • Jonathan Vandermause

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

  • Lixin Sun

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

  • Andrea Cepellotti

    • Harvard University
    • École Polytechnique Fédérale de Lausanne
    • School of Engineering and Applied Sciences, Harvard University
    • Materials Science & Mechanical Engineering, Harvard University

  • Boris Kozinsky

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

  View abstract →

• Nuclear quantum delocalization enhances non-covalent intramolecular interactions: A machine learning and path integral molecular dynamics study

  ORAL

  March 4, 2020, 4:42 PM – March 4, 2020, 4:54 PM

  Presenters

  • Huziel Sauceda

    • Tech Univ Berlin
    • Machine Learning Group, Technische Universität Berlin

  Authors

  • Huziel Sauceda

    • Tech Univ Berlin
    • Machine Learning Group, Technische Universität Berlin

  • Valentin Vassilev Galindo

    • Physics and Materials Science Research Unit, University of Luxembourg
    • University of Luxembourg Limpertsberg

  • Stefan Chmiela

    • Tech Univ Berlin
    • Machine Learning Group, Technische Universität Berlin

  • Klaus-Robert Müller

    • Tech Univ Berlin
    • Machine Learning Group, Technische Universität Berlin

  • Alexandre Tkatchenko

    • Physics and Materials Science Reasearch Unit, University of Luxembourg
    • Physics and Materials Science Research Unit, University of Luxembourg
    • University of Luxembourg
    • University of Luxembourg Limpertsberg

  View abstract →

• Active learning identifies optimal π-conjugated peptide chemistries for optoelectronics

  ORAL

  March 4, 2020, 4:54 PM – March 4, 2020, 5:06 PM

  Presenters

  • Kirill Shmilovich

    • University of Chicago

  Authors

  • Kirill Shmilovich

    • University of Chicago

  • Andrew L Ferguson

    • University of Chicago

  View abstract →

• A Self-consistent Artificial Neural Network Inter-atomic Potential for Li/C Systems

  ORAL

  March 4, 2020, 5:06 PM – March 4, 2020, 5:18 PM

  Presenters

  • Yusuf Shaidu

    • International School for Advanced Studies

  Authors

  • Yusuf Shaidu

    • International School for Advanced Studies

  • Ruggero Lot

    • International School for Advanced Studies

  • Franco Pellegrini

    • Laboratoire de Physique Statistique, École Normale Supérieure, Université PSL

  • Emine Kucukbenli

    • Harvard University

  • Stefano de Gironcoli

    • International School for Advanced Studies

  View abstract →

• Active Learning Driven Machine Learning Inter-Atomic Potentials Generation: A Case Study for Hafnium dioxide

  ORAL

  March 4, 2020, 5:18 PM – March 4, 2020, 5:30 PM

  Presenters

  • Ganesh Sivaraman

    • Argonne Leadership Computing Facility, Argonne National Laboratory

  Authors

  • Ganesh Sivaraman

    • Argonne Leadership Computing Facility, Argonne National Laboratory

  • Anand Narayanan Krishnamoorthy

    • Institute for Computational Physics, University of Stuttgart

  • Matthias Baur

    • Institute for Computational Physics, University of Stuttgart

  • Christian L. Holm

    • Physics, University of stuttgart
    • Institute for Computational Physics, University of Stuttgart

  • Marius Stan

    • Applied Materials Division, Argonne National Laboratory

  • Gábor Csányi

    • Department of Engineering, University of Cambridge

  • Chris Benmore

    • X-ray Science Division, Argonne National Laboratory

  • Alvaro Vazquez-Mayagoitia

    • Argonne Leadership Computing Facility, Argonne National Laboratory
    • Argonne National Lab
    • Computational Science Division, Argonne National Laboratory

  View abstract →