TMS: First-principles and Molecular Dynamics III
FOCUS · J6 ·
Presentations
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Machine Learning Reactive Force Fields for an Atomistically-Resolved View into Shockwave-Driven Carbon Condensation
COFFEE_KLATCH · Invited
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Authors
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Rebecca Lindsey
- Lawrence Livermore Natl Lab
- LLNL
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Machine Learning of Interatomic Potentials for Shock Compression Phenomena
ORAL
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Authors
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Benjamin Nebgen
- Los Alamos National Laboratory
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Kipton Barros
- Los Alamos National Laboratory
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Leonid Burakovsky
- Los Alamos National Laboratory
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Saryu Fensin
- Los Alamos National Laboratory
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Timothy Germann
- Los Alamos National Laboratory
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Nicholas Lubbers
- Los Alamos National Laboratory
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Justin Smith
- Los Alamos National Laboratory
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Quantum-accurate SNAP carbon potential for MD shock simulations
ORAL
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Authors
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Jonathan Willman
- University of South Florida
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Ashley Williams
- University of South Florida
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Kien Nguyen Cong
- University of South Florida
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Mitchell Wood
- Sandia National Laboratories
- SNL
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Aidan Thompson
- Sandia National Laboratories
- SNL
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Ivan Oleynik
- University of South Florida
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Transferable kinetic Monte Carlo models of condensed phase high temperature chemistry learned from molecular dynamics data.
ORAL
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Authors
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Qian Yang
- University of Connecticut
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Enze Chen
- Stanford University
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Vincent Dufour-Decieux
- Stanford University
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Carlos Sing-Long
- Pontificia Universidad Catolica de Chile
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Rodrigo Freitas
- Stanford University
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Evan Reed
- Stanford University
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Machine-learning based multi-scale model for shock-particle interactions
ORAL
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Authors
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Oishik Sen
- University of Iowa
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Soren Taverniers
- San Diego State University
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Pratik Das
- University of Iowa
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Gustaaf Jacobs
- San Diego State University
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HS Udaykumar
- University of Iowa
- The University of Iowa
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