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AI for Materials Discovery I

FOCUS · MAR-M37 · ID: 3091489

Presentations

  • Accelerating the discovery of van der Waals quantum materials using AI

    ORAL · Invited

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    Publication: [1] T. D. Rhone et al., "Data-driven studies of magnetic two-dimensional materials," Sci. Rep., vol. 10, no. 1, p. 15795, 2020.<br>[2] Y. Xie, G. Tritsaris, O. Granas, and T. Rhone, "Data-Driven Studies of the Magnetic Anisotropy of Two-Dimensional Magnetic Materials," J. Phys. Chem. Lett., vol. 12, no. 50, pp. 12048–12054.<br>[3] R. Bhattarai, P. Minch, and T. D. Rhone, "Investigating magnetic van der Waals materials using data-driven approaches," J. Mater. Chem. C, vol. 11, p. 5601, 2023.<br>[4] T. D. Rhone et al., "Artificial Intelligence Guided Studies of van der Waals Magnets," Adv. Theory Simulations, vol. 6, no. 6, p. 2300019, 2023.<br>[5] P. Minch, R. Bhattarai, K. Choudhary, and T. D. Rhone, "Predicting magnetic properties of van der Waals magnets using graph neural networks," Phys. Rev. Mater., vol. 8, no. 11, p. 114002, Nov. 2024.

    Presenters

    • Trevor David Rhone

      Rensselaer Polytechnic Institute

    Authors

    • Trevor David Rhone

      Rensselaer Polytechnic Institute

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  • Accelerating Heusler Alloy Discovery Using Machine Learning

    ORAL

    –

    Presenters

    • Wasim Raja Mondal

      Middle Tennessee State University

    Authors

    • Wasim Raja Mondal

      Middle Tennessee State University

    • Riley Nold

      The University of Alabama

    • Adam J Hauser

      University of Alabama

    • Hanna Terletska

      Middle Tennessee State University

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  • Inverse Design of Quantum Materials by Monte Carlo Tree Search and First Principles Calculations

    ORAL

    –

    Presenters

    • Ying Wai Li

      Los Alamos National Laboratory, Los Alamos National Laboratory (LANL), Los Alamos National Lab

    Authors

    • Ying Wai Li

      Los Alamos National Laboratory, Los Alamos National Laboratory (LANL), Los Alamos National Lab

    • Shunshun Liu

      University of Virginia; Los Alamos National Laboratory

    • Max J Ortner

      Los Alamos National Laboratory

    • Kevin J Allen

      Rice University, Rice University; Los Alamos National Laboratory

    • Christopher A Lane

      Los Alamos National Lab, Los Alamos National Laboratory, Los Alamos National Laboratory (LANL)

    • Jian-Xin Zhu

      Los Alamos National Laboratory (LANL), Los Alamos National Laboratory

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  • Open Materials Generation (OMG): Bespoke Materials Generation with Stochastic Interpolants

    ORAL

    –

    Presenters

    • Thomas Egg

      New York University, New York University (NYU)

    Authors

    • Thomas Egg

      New York University, New York University (NYU)

    • Eric Fuemmeler

      University of Minnesota

    • Amit Gupta

      University of Minnesota

    • Philipp Hoellmer

      New York University, New York University (NYU)

    • Maya M Martirossyan

      New York University, Cornell University, Department of Materials Science and Engineering, Cornell University, Ithaca, NY; Center for Soft Matter Research, Department of Physics, New York University, New York, NY

    • Pawan Prakash

      University of Florida

    • Gregory Wolfe

      New York University, New York University (NYU)

    • Adrian E Roitberg

      University of Florida

    • George Karypis

      University of Minnesota

    • Mark K Transtrum

      Brigham Young University

    • Mingjie Liu

      University of Florida

    • Richard G Hennig

      University of Florida, Department of Materials Science and Engineering, University of Florida

    • Ellad B Tadmor

      University of Minnesota

    • Stefano Martiniani

      New York University (NYU)

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  • Evaluating the limits of the physics learned by a machine learning model

    ORAL

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    Publication: Evaluating the Limits of the Physics Learned by a Machine Learning Model by Dale, Li, DeCost, Hattrick-Simpers<br>Loss Landscape Analysis of Model Accuracy by Dale, Li, DeCost, Hattrick-Simpers<br>Trusted AI Toolkit for Scientists (TRAITS) by Dale, Yao, Hattrick-Simpers

    Presenters

    • Ashley Dale

      University of Toronto

    Authors

    • Ashley Dale

      University of Toronto

    • Kangming Li

      Acceleration Consortium, University of Toronto

    • Brian DeCost

      National Institute of Standards and Technology

    • Jason Hattrick-Simpers

      University of Toronto

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  • RAMBO: A Risk-Adjusted Multi-Level Bayesian Optimization for achievable and safe autonomous material discovery

    ORAL

    –

    Presenters

    • Arpan Biswas

      University of Tennessee-Knoxville, University of Tennessee

    Authors

    • Arpan Biswas

      University of Tennessee-Knoxville, University of Tennessee

    • Yongtao Liu

      Oak Ridge National Laboratory

    • Ganesh Narasimha

      Oak Ridge National Lab, Oak Ridge National Laboratory

    • Rama Krishnan Vasudevan

      Oak Ridge National Laboratory

    View abstract →