Emerging Trends in Molecular Dynamics Simulations and Machine Learning IV

Presentations

• Using Topological Constraints to Modify Polymer Materials

  Invited

  March 4, 2020, 4:30 PM – March 4, 2020, 5:06 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, 5:06 PM – March 4, 2020, 5:18 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, 5:18 PM – March 4, 2020, 5:30 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, 5:30 PM – March 4, 2020, 5:42 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, 5:42 PM – March 4, 2020, 5:54 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, 5:54 PM – March 4, 2020, 6:06 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, 6:06 PM – March 4, 2020, 6:18 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

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

  ORAL

  March 4, 2020, 6:18 PM – March 4, 2020, 6:30 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

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

  ORAL

  March 4, 2020, 6:30 PM – March 4, 2020, 6:42 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

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

  ORAL

  March 4, 2020, 6:42 PM – March 4, 2020, 6:54 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, 6:54 PM – March 4, 2020, 7:06 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, 7:06 PM – March 4, 2020, 7:18 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, 7:18 PM – March 4, 2020, 7:30 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 →