Emerging Trends in Molecular Dynamics Simulations and Machine Learning I
FOCUS · J45 · ID: 355236
Presentations
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Generative and Reinforcement Learning assisted Material Design
Invited
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Presenters
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Pankaj Rajak
- Argonne National Lab
- LCF, Argonne National Laboratory
Authors
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Pankaj Rajak
- Argonne National Lab
- LCF, Argonne National Laboratory
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Unbiasing machine learning for molecular dynamics: emphasising out-of-equilibrium geometries using clustering
ORAL
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Presenters
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Grégory Cordeiro Fonseca
- University of Luxembourg
Authors
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Grégory Cordeiro Fonseca
- University of Luxembourg
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Igor Poltavskyi
- University of Luxembourg Limpertsberg
- University of Luxembourg
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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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Challenges in developing an extremely accurate many-body force field
ORAL
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Presenters
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Elizabeth Decolvenaere
- D. E. Shaw Research
Authors
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Elizabeth Decolvenaere
- D. E. Shaw Research
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Rian Cort Kormos
- D. E. Shaw Research
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Alexander Donchev
- D. E. Shaw Research
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John L Klepeis
- D. E. Shaw Research
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David E. Shaw
- D. E. Shaw Research
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Uncertainty quantification in molecular simulations with dropout neural network potentials
ORAL
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Presenters
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Mingjian Wen
- University of California, Berkeley
Authors
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Mingjian Wen
- University of California, Berkeley
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Ellad B. Tadmor
- University of Minnesota, Twin Cities
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Improving Fidelity and Transferability of Machine-Learned Reactive Interatomic Models Through Active Learning
ORAL
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Presenters
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Rebecca Lindsey
- Lawrence Livermore Natl Lab
Authors
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Rebecca Lindsey
- Lawrence Livermore Natl Lab
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Laurence Fried
- Lawrence Livermore Natl Lab
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Nir Goldman
- Lawrence Livermore Natl Lab
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Sorin Bastea
- Lawrence Livermore Natl Lab
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Uncertainty quantification of classical interatomic potentials in OpenKIM database
ORAL
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Presenters
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Yonatan Kurniawan
- Brigham Young Univ - Provo
Authors
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Yonatan Kurniawan
- Brigham Young Univ - Provo
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Cody Petrie
- Brigham Young Univ - Provo
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Kinamo Jahali Williams
- Brigham Young Univ - Provo
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Mark Transtrum
- Brigham Young Univ - Provo
- Physics & Astronomy, Brigham Young University
- Brigham Young University
- Physics and Astronomy, Brigham Young University
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Molecular dynamics density and viscosity simulations of alkanes
ORAL
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Presenters
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Pavao Santak
- Univ of Cambridge
Authors
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Pavao Santak
- Univ of Cambridge
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Gareth Conduit
- Univ of Cambridge
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Study of the microstructure of amorphous silicon and its effect on Li transportation with neural network potential
ORAL
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Presenters
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Wenwen Li
- AIST
- National Institute of Advanced Industrial Science and Technology
Authors
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Wenwen Li
- AIST
- National Institute of Advanced Industrial Science and Technology
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Yasunobu Ando
- CD-FMat, AIST
- AIST
- National Institute of Advanced Industrial Science and Technology
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Relative entropy indicates an ideal concentration for structure-based coarse graining of binary mixtures
ORAL
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Presenters
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David Rosenberger
- Los Alamos National Laboratory
Authors
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David Rosenberger
- Los Alamos National Laboratory
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Nico F. A. van der Vegt
- Chemistry, TU Darmstadt
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Exploring, fitting, and characterizing the configuration space of materials with multiscale universal descriptors
ORAL
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Presenters
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Noam Bernstein
- United States Naval Research Laboratory
Authors
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Noam Bernstein
- United States Naval Research Laboratory
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Volker L Deringer
- Department of Chemistry, University of Oxford
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Gábor Csányi
- Department of Engineering, University of Cambridge
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Predictive Atomistic Simulations of Materials using SNAP Data-Driven Potentials
ORAL
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Presenters
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Aidan Thompson
- Sandia National Laboratories
- Computational Multiscale, Sandia National Laboratories
Authors
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Aidan Thompson
- Sandia National Laboratories
- Computational Multiscale, Sandia National Laboratories
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Mitchell Wood
- Sandia National Laboratories
- Computational Multiscale, Sandia National Laboratories
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Mary Alice Cusentino
- Sandia National Laboratories
- Computational Multiscale, Sandia National Laboratories
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Julien Tranchida
- Sandia National Laboratories
- Computational Multiscale, Sandia National Laboratories
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Nicholas Lubbers
- Computer, Computational and Statistical Sciences, Information Sciences, Los Alamos National Laboratory
- Computer Computational Statistical Sciences, Los Alamos National Laboratory
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Stan Moore
- Computational Multiscale, Sandia National Laboratories
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Rahul Gayatri
- Application Performance, NERSC, Lawrence Berkeley National Laboratory
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Accurate and Data-Efficient Machine Learning Force Fields for Periodic Systems
ORAL
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Presenters
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Luis Gálvez-González
- Programa de Doctorado en Ciencias (Física), Universidad de Sonora
Authors
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Luis Gálvez-González
- Programa de Doctorado en Ciencias (Física), Universidad de Sonora
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Huziel Sauceda
- Tech Univ Berlin
- Machine Learning Group, Technische Universität Berlin
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Stefan Chmiela
- Tech Univ Berlin
- Machine Learning Group, Technische Universität Berlin
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Alvaro Posada-Amarillas
- Departamento de Investigación en Física, Universidad de Sonora
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Lauro Oliver Paz-Borbón
- Instituto de Física, Universidad Nacional Autónoma de México
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Klaus-Robert Müller
- Tech Univ Berlin
- Machine Learning Group, Technische Universität Berlin
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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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Phase diagrams of nuclear pasta phases in neutron star matter
ORAL
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Presenters
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Jorge Munoz
- University of Texas, El Paso
Authors
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Jorge Munoz
- University of Texas, El Paso
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Jorge Alberto Lopez
- University of Texas, El Paso
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