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