Machine Learning and Neural Networks in Chemical Physics

Presentations

• Atomistic Line Graph Neural Network for Improved Materials Property Predictions

  ORAL

  March 17, 2022, 8:00 AM – March 17, 2022, 8:12 AM

  Presenters

  • Kamal Choudhary

    • National Institute of Standards and Tech

  Authors

  • Kamal Choudhary

    • National Institute of Standards and Tech

  • Brian DeCost

    • National Institute of Standards and Technology

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• Graph Neural Networks that incorporate Physical Structure

  ORAL

  March 17, 2022, 8:12 AM – March 17, 2022, 8:24 AM

  Publication: https://arxiv.org/abs/2103.01710

  Presenters

  • Erik Thiede

    • Flatiron Institute Center for Computational Mathematics

  Authors

  • Erik Thiede

    • Flatiron Institute Center for Computational Mathematics

  • Wenda Zhou

    • Flatiron Institute, CCM

  • Risi Kondor

    • University of Chicago

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• Data augmentation techniques to improve material property prediction performance using Graph Neural Networks

  ORAL

  March 17, 2022, 8:24 AM – March 17, 2022, 8:36 AM

  Publication: Planned Paper:
  Auglichem: Data Augmentation Library of Chemical Structures for Machine Learning

  Presenters

  • Rishikesh Magar

    • Carnegie Mellon University

  Authors

  • Rishikesh Magar

    • Carnegie Mellon University

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• Kinetics studies of gas phase reactions using neural network potentials

  ORAL

  March 17, 2022, 8:36 AM – March 17, 2022, 8:48 AM

  Presenters

  • Adrian Gordon

    • University of Minnesota

  Authors

  • Adrian Gordon

    • University of Minnesota

  • Jason D Goodpaster

    • University of Minnesota

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• Yet Another Reaction Prediction v2.0: Advances in Automatic Reaction Prediction and Establishment of Benchmark Systems

  ORAL

  March 17, 2022, 8:48 AM – March 17, 2022, 9:00 AM

  Publication: Zhao, Q. and Savoie, B.M., 2021. Simultaneously improving reaction coverage and computational cost in automated reaction prediction tasks. Nature Computational Science, 1(7), pp.479-490.

  Presenters

  • Qiyuan Zhao

    • Purdue University

  Authors

  • Qiyuan Zhao

    • Purdue University

  • Brett M Savoie

    • Purdue University

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• Predicting the density of states of crystalline materials via machine learning

  ORAL

  March 17, 2022, 9:00 AM – March 17, 2022, 9:12 AM

  Publication: "Density of States Prediction for Materials Discovery via Contrastive Learning from Probabilistic Embeddings", Shufeng Kong, Francesco Ricci, Dan Guevarra, Jeffrey B. Neaton, Carla P. Gomes, and John M. Gregoire, submitted to Nat.Commun. and on arXiv at: http://arxiv.org/abs/2110.11444

  Presenters

  • Francesco Ricci

    • UCLouvain
    • Lawrence Berkeley National Laboratory

  Authors

  • Francesco Ricci

    • UCLouvain
    • Lawrence Berkeley National Laboratory

  • Shufeng Kong

    • Department of Computer Science, Cornell University, Ithaca, NY, USA

  • Dan Guevarra

    • Caltech
    • Division of Engineering and Applied Science, California Institute of Technology, Pasadena, CA, USA

  • Carla P Gomes

    • Cornell
    • Cornell University
    • Department of Computer Science, Cornell University, Ithaca, NY, USA

  • John M Gregoire

    • Caltech
    • Division of Engineering and Applied Science, California Institute of Technology, Pasadena, CA, USA

  • Jeffrey B Neaton

    • Lawrence Berkeley National Laboratory
    • University of California, Berkeley; Lawrence Berkeley National Laboratory; Kavli Energy NanoSciences Institute at Berkeley
    • Department of Physics, University of California, Berkeley; Materials Sciences Division, Lawrence Berkeley National Laboratory; Kavli Energy NanoScience Institute at Berkeley
    • Department of Physics, University of California, Berkeley, CA 94720; Materials Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720; Kavli Energy Nano

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• Machine learning Kohn-Sham potentials in time-dependent density functional theory

  ORAL

  March 17, 2022, 9:12 AM – March 17, 2022, 9:24 AM

  Publication: Yang, J., Whitfield, J. D. Machine learning Kohn-Sham potentials in time-dependent density functional theory

  Presenters

  • Jun Yang

    • Dartmouth College

  Authors

  • Jun Yang

    • Dartmouth College

  • James D Whitfield

    • Dartmouth College

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• Machine learning methodologies for accurate electron correlation energies and potential energy surfaces.

  ORAL

  March 17, 2022, 9:24 AM – March 17, 2022, 9:36 AM

  Presenters

  • Jason D Goodpaster

    • University of Minnesota

  Authors

  • Jason D Goodpaster

    • University of Minnesota

  • Clara Kirkvold

    • University of Minnesota

  • Andrew M Johannesen

    • University of Minnesota

  • Quin H Hu

    • University of Minnesota

  • Adrian Gordon

    • University of Minnesota

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• Size-Extensivity of Machine Learning Potentials for Molecules

  ORAL

  March 17, 2022, 9:36 AM – March 17, 2022, 9:48 AM

  Presenters

  • Murat Keceli

    • Argonne National Laboratory

  Authors

  • Murat Keceli

    • Argonne National Laboratory

  • Alvaro Vazquez-Mayagoitia

    • Argonne National Laboratory

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• Semi-Local Density Fingerprints for Machine Learning Molecular Properties, Intra-/Inter-molecular Interactions, and Chemical Reactions

  ORAL

  March 17, 2022, 9:48 AM – March 17, 2022, 10:00 AM

  Presenters

  • Yang Yang

    • Cornell University

  Authors

  • Yang Yang

    • Cornell University

  • Zachary M Sparrow

    • Cornell University

  • Brian G Ernst

    • Cornell University

  • Trine K Quady

    • Cornell University

  • Justin Lee

    • Cornell University

  • Yan Yang

    • Cornell University

  • Lijie Tu

    • Cornell University

  • Robert A Distasio

    • Cornell University

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• Unsupervised machine learning approach for detecting second order phase transition in three-dimensional liquid mixtures

  ORAL

  March 17, 2022, 10:00 AM – March 17, 2022, 10:12 AM

  Presenters

  • Inhyuk Jang

    • University of Wisconsin-Madison

  Authors

  • Inhyuk Jang

    • University of Wisconsin-Madison

  • Supreet Kaur

    • University of Wisconsin - Madison

  • Arun Yethiraj

    • University of Wisconsin - Madison

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• Fully Automated Nanoscale to Atomistic Structure from Theory and X-Ray Spectroscopy Experiments

  ORAL

  March 17, 2022, 10:12 AM – March 17, 2022, 10:24 AM

  Presenters

  • Davis G Unruh

    • Argonne National Laboratory

  Authors

  • Davis G Unruh

    • Argonne National Laboratory

  • Chaitanya Kolluru

    • Argonne National Laboratory

  • Eli D Kinigstein

    • Argonne National Laboratory

  • Xiaoyi Zhang

    • Argonne National Laboratory

  • Maria K Chan

    • Argonne National Laboratory

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• Comprehensive Analysis of Machine-Learning Kernels for Predicting Molecular Properties

  ORAL

  March 17, 2022, 10:24 AM – March 17, 2022, 10:36 AM

  Presenters

  • Mirela Puleva

    • University of Luxembourg Limpertsberg

  Authors

  • Mirela Puleva

    • University of Luxembourg Limpertsberg

  • Leonardo Medrano Sandonas

    • University of Luxembourg Limpertsberg
    • University of Luxembourg

  • Artem Kokorin

    • University of Luxembourg Limpertsberg

  • Alexandre Tkatchenko

    • University of Luxembourg Limpertsberg

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• Machine learning density functionals from the random-phase approximation

  ORAL

  March 17, 2022, 10:36 AM – March 17, 2022, 10:48 AM

  Publication: S. Riemelmoser et al., Machine learning density functionals from the random-phase approximation (unpublished)

  Presenters

  • Stefan Riemelmoser

    • Univ of Vienna

  Authors

  • Stefan Riemelmoser

    • Univ of Vienna

  • Carla Verdi

    • Univ of Vienna
    • University of Vienna

  • Merzuk Kaltak

    • VASP Software GmbH

  • Georg Kresse

    • Univ of Vienna
    • University of Vienna

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