Designing Neural Networks for the Structure of Physics Data
FOCUS · K53 · ID: 1067006
Presentations
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Unified Graph Neural Network Force-field for the Periodic Table
ORAL · Invited
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Presenters
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Kamal Choudhary
National Institute of Standards and Tech
Authors
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Kamal Choudhary
National Institute of Standards and Tech
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Structure-motif-based material network for functional material discovery.
ORAL
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Presenters
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Anoj Aryal
Northeastern University
Authors
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Anoj Aryal
Northeastern University
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Huta Banjade
Virginia Commonwealth University
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Qimin Yan
Northeastern University
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Physically informed graph neural networks for prediction of optical properties of solid materials
ORAL
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Presenters
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Can Ataca
University of Maryland, Baltimore County
Authors
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Can Ataca
University of Maryland, Baltimore County
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Akram Ibrahim
University of Maryland Baltimore County
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Mario Geiger
ORAL · Invited
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Presenters
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Mario Geiger
MIT, Swiss Federal Institute of Technology Lausanne (EPFL )
Authors
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Mario Geiger
MIT, Swiss Federal Institute of Technology Lausanne (EPFL )
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Understanding Self-Assembly Behavior with Self-Supervised Learning
ORAL
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Publication: https://arxiv.org/abs/2110.02393 ; also a more tailored preprint that is under review as of submission
Presenters
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Matthew Spellings
Vector Institute for Artificial Intelligence
Authors
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Matthew Spellings
Vector Institute for Artificial Intelligence
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Maya Martirossyan
Cornell University
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Julia Dshemuchadse
Cornell University
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Metric geometry tools for automatic structure phase map generation
ORAL
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Presenters
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Kiran Vaddi
University of Washington
Authors
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Kiran Vaddi
University of Washington
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Karen Li
University of Washington
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Lilo Pozzo
University of Washington
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Graph neural network accelerated generalizable stress field prediction for mesh-based finite element simulations
ORAL
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Presenters
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Bowen Zheng
University of California at Berkeley, University of California, Berkeley
Authors
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Bowen Zheng
University of California at Berkeley, University of California, Berkeley
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Zeqing Jin
University of California, Berkeley
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Changgon Kim
Hyundai Motor Company
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Grace X Gu
University of California at Berkeley, University of California, Berkeley
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Modeling the Band Structure of Periodic Crystals with Physics-Informed Neural Networks
ORAL
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Presenters
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Circe Hsu
Northeastern University
Authors
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Circe Hsu
Northeastern University
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Daniel T Larson
Harvard University, Department of Physics, Harvard University
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Gabriel R Schleder
Harvard University
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Marios Mattheakis
Harvard University
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Efthimios Kaxiras
Harvard University
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Using CycleGANs to construct training data for other Machine Learning models
ORAL
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Presenters
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Abid A Khan
University of Illinois at Urbana-Champai
Authors
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Abid A Khan
University of Illinois at Urbana-Champai
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Chia-Hao Lee
University of Illinois at Urbana-Champaign
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Pinshane Y Huang
University of Illinois at Urbana-Champaign
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Bryan K Clark
University of Illinois at Urbana-Champaign
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Contrastive Learning Reveals the Trajectory of Protein Structure Evolution
ORAL
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Presenters
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Yong Wei
High Point University
Authors
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Yong Wei
High Point University
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Baofu Qiao
City University of New York
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Tao Wei
Howard University
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Hanning Chen
The University of Texas at Austin
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Geometric Dynamic Variational Autoencoders for Learning Nonlinear Dynamics
ORAL
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Publication: Title: GD-VAEs: Geometric Dynamic Variational Autoencoders for Learning Nonlinear Dynamics and Dimension Reductions. Authors: Ryan Lopez and Paul J. Atzberger. (Submitted to JMLR, arxiv link: https://arxiv.org/pdf/2206.05183.pdf)
Presenters
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Ryan Lopez
Massachusetts Institute of Technology
Authors
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Ryan Lopez
Massachusetts Institute of Technology
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Paul J Atzberger
University of California, Santa Barbara
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