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Material Science and Machine Learning III

FOCUS · Z32 · ID: 48677

Presentations

  • Development of machine learning framework to fit quantum-mechanical ab-initio potential energy surface to a cite-cite molecular potential

    ORAL

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    Presenters

    • Bhanuday Sharma

      Indian Institute of Technology Kanpur

    Authors

    • Bhanuday Sharma

      Indian Institute of Technology Kanpur

    • Savitha Pareek

      DELL HPC and AI Innovation Lab, Bengaluru, India

    • Ashish K Singh

      DELL HPC and AI Innovation Lab, Bengaluru, India

    • Rakesh Kumar

      Indian Institute of Technology Kanpur, India, Indian Institute of Technology Kanpur

    View abstract →

  • Machine learning the saling property of density functionals via data augmentation

    ORAL

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    Presenters

    • Weiyi Gong

      Temple University

    Authors

    • Weiyi Gong

      Temple University

    • Tao Sun

      Stony Brook University

    • Peng Chu

      Temple University

    • Hexin Bai

      Temple University

    • Anoj Aryal

      Temple University

    • Shah Tanvir-Ur-Rahman Chowdhury

      Temple University

    • Jie Yu

      Temple University

    • Haibin Ling

      Stony Brook University

    • John P Perdew

      Temple University, Departments of Physics and Chemistry, Temple U., Philadelphia, PA 19122

    • Qimin Yan

      Temple University

    View abstract →

  • Benchmarking Descriptors, Models, and Systems for Many-Body Machine Learned Force Fields in Molten Transition Metals

    ORAL

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    Presenters

    • Cameron J Owen

      Harvard University

    Authors

    • Cameron J Owen

      Harvard University

    • Steven B Torrisi

      Harvard University, Toyota Research Institute, Harvard University

    • Isabel Diersen

      Harvard University

    • Lixin Sun

      Harvard University

    • Jin Soo Lim

      Harvard University

    • Yu Xie

      Harvard University

    • Jonathan P Vandermause

      Harvard University

    • Boris Kozinsky

      Harvard University

    View abstract →

  • Zirconium Machine Learned Potential Trained on a Euclidean Neural Network

    ORAL

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    Presenters

    • Vanessa J Meraz

      University of Texas at El Paso

    Authors

    • Vanessa J Meraz

      University of Texas at El Paso

    • Sofia G Gomez

      University of Texas at El Paso

    • Valeria I Arteaga Muniz

      University of Texas at El Paso

    • Adrian De la Rocha Galán

      University of Texas at El Paso

    • Tess E Smidt

      Massachusetts Institute of Technology

    • Sara Kadkhodaei

      University of Illinois at Chicago

    • Bert A de Jong

      Lawrence Berkeley National Laboratory, LBNL

    • Jorge A Munoz

      University of Texas at El Paso

    View abstract →

  • A machine learning-based interatomicpotential for Fe using marginalized graph kernels

    ORAL

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    Presenters

    • Valeria I Arteaga Muniz

      University of Texas at El Paso

    Authors

    • Valeria I Arteaga Muniz

      University of Texas at El Paso

    • Adrian De la Rocha Galán

      University of Texas at El Paso

    • Vanessa J Meraz

      University of Texas at El Paso

    • Yu-Hang Tang

      Lawrence Berkeley National Laboratory

    • Ramon J Ravelo

      University of Texas at El Paso

    • Bert A de Jong

      Lawrence Berkeley National Laboratory, LBNL

    • Jorge A Munoz

      University of Texas at El Paso

    View abstract →

  • TitleOptimization of prediction model for elastic constants of high entropy alloys by using LIDG method

    ORAL

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    Presenters

    • Genta Hayashi

      Osaka Univ.

    Authors

    • Genta Hayashi

      Osaka Univ.

    • Katsuhiro Suzuki

      Osaka Univ., Osaka University, Division of Materials and Manufacturing Science, Osaka University

    • Tomoyuki Terai

      Osaka Univ.

    • Kazunori Sato

      Osaka Univ., Osaka University, Division of Materials and Manufacturing Science, Osaka University, Osaka University, CSRN-Osaka

    View abstract →

  • Building Chemical Property Models for Energetic Materials from Small Datasets using a Transfer Learning Approach

    ORAL

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    Presenters

    • Brian C Barnes

      DEVCOM Army Research Laboratory, US Army Research Lab Aberdeen

    Authors

    • Brian C Barnes

      DEVCOM Army Research Laboratory, US Army Research Lab Aberdeen

    • Joshua L Lansford

      DEVCOM Army Research Laboratory

    • Betsy M Rice

      DEVCOM Army Research Laboratory

    • Klavs F Jensen

      Massachusetts Institute of Technology

    View abstract →

  • Deep Learning Analysis of Polaritonic Wave Images

    ORAL

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    Presenters

    • Suheng Xu

      Columbia University

    Authors

    • Suheng Xu

      Columbia University

    • Alexander S McLeod

      Columbia Univ, Columbia University

    • Xinzhong Chen

      Stony Brook University (SUNY), State Univ of NY - Stony Brook

    • Daniel J Rizzo

      Columbia University

    • Bjarke S Jessen

      Columbia University

    • Ziheng Yao

      State Univ of NY - Stony Brook, Stony Brook University (SUNY)

    • Zhicai Wang

      State Univ of NY - Stony Brook

    • Zhiyuan Sun

      Columbia Univ, Harvard University, Columbia University

    • Sara Shabani

      Columbia University

    • Abhay N Pasupathy

      Columbia University, Brookhaven National Laboratory & Columbia University

    • Andrew J Millis

      Columbia University, Columbia University; Flatiron Institute, Columbia University, Flatiron Institute

    • Cory R Dean

      Columbia University, Columbia Univ

    • James C Hone

      Columbia University

    • Mengkun Liu

      State Univ of NY - Stony Brook

    • Dmitri N Basov

      Columbia University

    View abstract →

  • Thermal Transport with Message Passing Neural Networks via the Green-Kubo Method

    ORAL

    –

    Publication: M.F. Langer, F. Knoop, C. Carbogno, M. Scheffler, and M. Rupp, in preparation

    Presenters

    • Marcel F Langer

      Machine Learning Group, Technische Universität Berlin and NOMAD Laboratory, Fritz Haber Institute of the Max Planck Society

    Authors

    • Marcel F Langer

      Machine Learning Group, Technische Universität Berlin and NOMAD Laboratory, Fritz Haber Institute of the Max Planck Society

    • Florian Knoop

      NOMAD Laboratory, Fritz Haber Institute of the Max Planck Society, The NOMAD Laboratory at the Fritz Haber Institute of the MPG

    • Christian Carbogno

      NOMAD Laboratory, Fritz Haber Institute of the Max Planck Society, Fritz-Haber-Institute, The NOMAD Laboratory at the Fritz Haber Institute of the MPG

    • Matthias Scheffler

      NOMAD Laboratory, Fritz Haber Institute of the Max Planck Society, Fritz-Haber Institute, The NOMAD Laboratory at the Fritz Haber Institute of the MPG

    • Matthias Rupp

      Department of Computer and Information Science, University of Konstanz and NOMAD Laboratory, Fritz Haber Institute of the Max Planck Society

    View abstract →