APS March Meeting 2020
Volume 65, Number 1
Monday–Friday, March 2–6, 2020;
Denver, Colorado
Session Index
Session M45: Emerging Trends in Molecular Dynamics Simulations and Machine Learning III
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Sponsoring Units: DCOMP GDS DSOFT DPOLY
Chair: Dvora Perihia, Clemson University
Room: 706
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M45.00001: The Self Learning Kinetic Monte Carlo (SLKMC) method augmented with data analytics for adatom-island diffusion on surfaces
Invited Speaker:
Talat Rahman
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M45.00002: Accelerated Discovery of Dielectric Polymer Materials Using Graph Convolutional Neural Networks
Ankit Mishra, Pankaj Rajak, Ekin Dogus Cubuk, Ken-ichi Nomura, Rajiv Kalia, Aiichiro Nakano, Ajinkya Deshmukh, Lihua Chen, Greg Sotzing, Yang Cao, Ramamurthy Ramprasad, Priya Vashishta
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M45.00003: Deep Learning embedding layers for better prediction of atomic forces in solids
Sivan Niv, Goren Gordon, Amir Natan
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M45.00004: A molecular dynamics study of water crystallization using deep neural network potentials of ab-initio quality
Pablo Piaggi, Roberto Car
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M45.00005: Machine learning force field using decomposed atomic energies from ab initio calculations
Lin-Wang Wang
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M45.00006: Machine learning to derive quantum-informed and chemically-aware force fields to simulate interfaces and defects in hybrid halide perovskites
Ross E Larsen, Matthew Jankousky, Derek Vigil-Fowler, Aaron M Holder, K. Grace Johnson
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M45.00007: Active Learning of Coarse Grained Force Fields with Gaussian Process Regression
Blake Duschatko, Jonathan Vandermause, Nicola Molinari, Boris Kozinsky
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M45.00008: External Potential Ensembles to Improve the Learning of Transferable Coarse-Grained Potentials
Kevin Shen, Kris T Delaney, M. Scott Shell, Glenn H Fredrickson
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M45.00009: Data-driven parameterization of coarse-grained models of soft materials using machine learning tools
Lilian Johnson, Frederick Phelan
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M45.00010: JAX, M.D.
End-to-End Differentiable, Hardware Accelerated, Molecular Dynamics in Pure Python
Sam Schoenholz, Ekin Dogus Cubuk
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M45.00011: A neural network interatomic potential for molten NaCl
Qingjie Li, Emine Kucukbenli, Stephen Lam, Boris Khaykovich, Efthimios Kaxiras, Ju Li
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M45.00012: Simulating Aluminum Corrosion Using DFT Trained Deep Neural Network Potentials
Wissam A Saidi, Shyam Dwaraknath
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M45.00013: Tensor-Field Molecular Dynamics: A Deep Learning model for highly accurate, symmetry-preserving force-fields from small data sets
Simon Batzner, Lixin Sun, Tess E Smidt, Boris Kozinsky
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