Machine Learning and Neural Networks in Chemical Physics

Presentations

• Atomistic Line Graph Neural Network for Improved Materials Property Predictions

  ORAL

  March 17, 2022, 9:00 AM – March 17, 2022, 9:12 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, 9:12 AM – March 17, 2022, 9:24 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, 9:24 AM – March 17, 2022, 9:36 AM

  Publication: Planned Paper: <br>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, 9:36 AM – March 17, 2022, 9:48 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, 9:48 AM – March 17, 2022, 10:00 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, 10:00 AM – March 17, 2022, 10:12 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, 10:12 AM – March 17, 2022, 10:24 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, 10:24 AM – March 17, 2022, 10:36 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, 10:36 AM – March 17, 2022, 10:48 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, 10:48 AM – March 17, 2022, 11:00 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, 11:00 AM – March 17, 2022, 11:12 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, 11:12 AM – March 17, 2022, 11:24 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, 11:24 AM – March 17, 2022, 11:36 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, 11:36 AM – March 17, 2022, 11:48 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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