Emerging Trends in Molecular Dynamics Simulations and Machine Learning III
FOCUS · K62 · ID: 1067986
Presentations
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Reinforcement Learning Agent for autonomous predictive material synthesis and transport pathways
ORAL · Invited
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Presenters
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ankit mishra
- University of Southern California
- Univ of Southern California
Authors
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ankit mishra
- University of Southern California
- Univ of Southern California
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Judicious Curation of DFT Machine Learning Datasets for Accurate, Flexible, and Transferrable Atomistic Potentials for Elemental Systems and Metal Oxides
ORAL
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Publication: Christopher M. Andolina, Marta Bon, Daniele Passerone, and Wissam A. Saidi; J. Phys. Chem. C 2021, 125, 31, 17438–17447
Christopher M. Andolina, Philip Williamson, and Wissam A. Saidi; J. Chem. Phys. 2020, 152, 154701
Christopher M. Andolina, Jacob G. Wright, Nishith Das, and Wissam A. Saidi; 2021, Phys. Rev. Materials 5, 083804
Author Dylan Bayerl, Christopher M. Andolina, Shyam Dwaraknath and Wissam A. Saidi, Digital Discovery, 2022,1, 61-69Presenters
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Wissam A Saidi
- University of Pittsburgh
- National Energy Technology Laboratory
Authors
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Wissam A Saidi
- University of Pittsburgh
- National Energy Technology Laboratory
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Christopher M Andolina
- Univeristy of Pittsburgh
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Pandu Wisesa
- University of Pittsburgh
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Probing Thermomechanical Properties of Two-dimensional van der Waals Architectures Using Surface Acoustic Waves
ORAL
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Presenters
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Anikeya Aditya
- University of Southern California
Authors
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Anikeya Aditya
- University of Southern California
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Nitish Baradwaj
- University of Southern California
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ankit mishra
- University of Southern California
- Univ of Southern California
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Ken-ichi Nomura
- University of Southern California
- Univ of Southern California
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Aiichiro Nakano
- University of Southern California
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Priya Vashishta
- University of Southern California
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Rajiv K Kalia
- Univ of Southern California
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An unsupervised data mining methodology for analysis of molecular dynamics sampling of local coordination
ORAL
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Presenters
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Fabrice Roncoroni
- Lawrence Berkeley National Laboratory
- Lawrence Berkeley National lab
Authors
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Fabrice Roncoroni
- Lawrence Berkeley National Laboratory
- Lawrence Berkeley National lab
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Ana Sanz Matias
- Lawrence Berkeley National Laboratory
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Siddharth Sundarararaman
- Lawrence Berkeley National Laboratory
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David Prendergast
- Lawrence Berkeley National Laboratory
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Evaluating robustness of machine learned force fields with enhanced sampling methods
ORAL
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Presenters
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Gustavo R Perez Lemus
- University of Chicago
Authors
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Gustavo R Perez Lemus
- University of Chicago
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Juan J De Pablo
- University of Chicago
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Pablo Zubieta
- The University of Chicago
- Pritzker School of Molecular Engineering
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Yezhi Jin
- The University of Chicago
- The University Of Chicago
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Structure and Dielectric Properties of Water and Aqueous Solutions Using Neural Network Quantum Molecular Dynamics
ORAL
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Presenters
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RURU MA
- University of Southern California
Authors
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RURU MA
- University of Southern California
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Aravind Krishnamoorthy
- University of Southern California
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Nitish Baradwaj
- University of Southern California
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Ken-ichi Nomura
- University of Southern California
- Univ of Southern California
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Kohei Shimamura
- Kumamoto University
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Pankaj Rajak
- University of Southern California
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Fuyuki Shimojo
- Kumamoto University
- Kumamoto Univ
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Aiichiro Nakano
- University of Southern California
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Rajiv K Kalia
- Univ of Southern California
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Priya Vashishta
- University of Southern California
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HubbardNet: efficient predictions of the Bose-Hubbard model spectrum with deep neural networks
ORAL · Invited
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Publication: HubbardNet: efficient predictions of the Bose-Hubbard model spectrum with deep neural networks, submitted to the Machine Learning and the Physical Sciences Workshop at the 36th conference on Neural Information Processing Systems (NeurIPS); planning to submit to Physical Review Research
Presenters
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Ziyan Zhu
- Stanford University
Authors
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Ziyan Zhu
- Stanford University
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Ab initio-based deep potential simulation of 2D confined water
ORAL
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Presenters
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Iman Ahmadabadi
- University of Maryland, College Park
Authors
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Iman Ahmadabadi
- University of Maryland, College Park
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Marcos Calegari Andrade
- Lawrence Livermore National Lab
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Pablo M Piaggi
- Princeton University
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Roberto Car
- Princeton University
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Thermodynamics and Phase Behavior of Alkali Metal Mixture Using Ab-initio-based Machine Learning Interatomic Potentials
ORAL
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Presenters
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Ayu Irie
- Kumamoto University
Authors
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Ayu Irie
- Kumamoto University
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Akihide Koura
- Kumamoto University
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Kohei Shimamura
- Kumamoto University
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Fuyuki Shimojo
- Kumamoto University
- Kumamoto Univ
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Physically and chemically inspired kernel-based neural network for constructing accurate and efficient machine learning force fields for hundreds of atoms.
ORAL
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Presenters
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Igor Poltavskyi
- University of Luxembourg
Authors
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Igor Poltavskyi
- University of Luxembourg
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Anton Charkin-Gorbulin
- University of Mons
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Artem Kokorin
- University of Luxembourg
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Alexandre Tkatchenko
- University of Luxembourg
- University of Luxembourg Limpertsberg
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Grgory Cordeiro Fonseca
- University of Luxembourg Limpertsberg
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Bonded Potential Dynamics in Chemically-Specific Coarse-Grained Models of Polymers
ORAL
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Publication: Lilian C. Johnson and Frederick R. Phelan Jr., Dynamically consistent coarse-grain simulation model of chemically specific polymer melts via friction parameterization, J. Chem. Phys. 154, 084114 (2021).
Presenters
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Frederick R Phelan
- National Institute of Standards and Technology (NIST)
Authors
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Frederick R Phelan
- National Institute of Standards and Technology (NIST)
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Lilian C Johnson
- National Institute of Standards and Technology
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