Machine Learning of Molecules and Materials: Materials I

Presentations

• Overcoming the limits of approximate electronic structure models in machine learning accelerated materials discovery

  ORAL · Invited

  March 5, 2024, 3:00 PM – March 5, 2024, 3:36 PM

  Presenters

  • Heather Kulik

    • MIT

  Authors

  • Heather Kulik

    • MIT

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• Accelerating Computational Chemistry and Materials Science Research with Azure Quantum Elements

  ORAL

  March 5, 2024, 3:36 PM – March 5, 2024, 3:48 PM

  Presenters

  • Martin Suchara

    • Microsoft Corporation

  Authors

  • Martin Suchara

    • Microsoft Corporation

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• Accelerating materials discovery using integrated deep machine learning approaches

  ORAL

  March 5, 2024, 3:48 PM – March 5, 2024, 4:00 PM

  Presenters

  • Weiyi Xia

    • Ames National Laboratory

  Authors

  • Weiyi Xia

    • Ames National Laboratory

  • Ling Tang

    • Zhejiang University of Technology

  • Huaijun Sun

    • Zhejiang Agriculture and Forestry University

  • Chao Zhang

    • Yantai University

  • Kai-Ming Ho

    • Iowa State University

  • Gayatri Viswanathan

    • Iowa State University

  • Kirill Kovnir

    • Iowa State
    • Iowa State University
    • Department of Chemistry, Iowa State University; Ames National Laboratory (U.S. DOE)

  • Cai-Zhuang Wang

    • Ames National Laboratory
    • Iowa State University

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• Equivariant Graph Neural Networks for Predicting Spin-Crossover Energy in Transition Metal Complexes

  ORAL

  March 5, 2024, 4:00 PM – March 5, 2024, 4:12 PM

  Presenters

  • Angel M Albavera Mata

    • University of Florida

  Authors

  • Angel M Albavera Mata

    • University of Florida

  • Eric C Fonseca

    • University of Florida

  • Pawan Prakash

    • University of Florida

  • Samuel B Trickey

    • University of Florida

  • Richard G Hennig

    • University of Florida

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• Incorporating explicit electrostatic interactions in machine learning potentials

  ORAL

  March 5, 2024, 4:12 PM – March 5, 2024, 4:24 PM

  Presenters

  • Max Veit

    • Aalto University

  Authors

  • Max Veit

    • Aalto University

  • Miguel Caro

    • Aalto University

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• Designing Coarse-Grained Representations for Soft Materials using Attentive Message-Passing

  ORAL

  March 5, 2024, 4:24 PM – March 5, 2024, 4:36 PM

  Publication: J. Charlie Maier, Chun-I Wang, and Nicholas E. Jackson, "Distilling Coarse-Grained Representations of Molecular Electronic Structure with Continuously Gated Message Passing" [under review]

  Presenters

  • John C Maier

    • University of Illinois at Urbana-Champaign

  Authors

  • John C Maier

    • University of Illinois at Urbana-Champaign

  • Chun-I Wang

    • University of Illinois, Urbana-Champaign

  • Nick E Jackson

    • Argonne National Laboratory
    • University of Illinois at Urbana-Champaign

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• ML Gradients in Molecular Simulations

  ORAL · Invited

  March 5, 2024, 4:36 PM – March 5, 2024, 5:12 PM

  Presenters

  • Rafael Gomez-Bombarelli

    • MIT

  Authors

  • Rafael Gomez-Bombarelli

    • MIT

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• Transferable diversity – a data-driven representation of chemical space

  ORAL

  March 5, 2024, 5:12 PM – March 5, 2024, 5:24 PM

  Publication: Gould T, Chang B, Dale S., Vuckovic S: Transferable diversity – a data-driven representation of chemical space. ChemRxiv. Cambridge: Cambridge Open Engage; 2023; [https://chemrxiv.org/engage/chemrxiv/article-details/6511601aed7d0eccc32e3ace]

  Presenters

  • Stefan Vuckovic

    • University of Fribourg

  Authors

  • Stefan Vuckovic

    • University of Fribourg

  • Tim Gould

    • Griffith University

  • Bun Chan

    • Graduate School of Engineering, Nagasaki University, Bunkyo 1-14, Nagasaki 852-8521, Japan

  • Stephen G Dale

    • Dalhousie Univ

  • Stephen G Dale

    • Dalhousie Univ

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• Active-Learning for Machine-Learned Interatomic Potentials; The Example of Strongly Anharmonic Materials

  ORAL

  March 5, 2024, 5:24 PM – March 5, 2024, 5:36 PM

  Presenters

  • Kisung Kang

    • The NOMAD Laboratory at the FHI of the Max-Planck-Gesellschaft and IRIS-Adlershof of the Humboldt-Universität zu Berlin

  Authors

  • Kisung Kang

    • The NOMAD Laboratory at the FHI of the Max-Planck-Gesellschaft and IRIS-Adlershof of the Humboldt-Universität zu Berlin

  • Christian Carbogno

    • The NOMAD Laboratory at the FHI of the Max-Planck-Gesellschaft and IRIS-Adlershof of the Humboldt-Universität zu Berlin
    • The NOMAD Laboratory at the FHI of the Max Planck Society

  • Matthias Scheffler

    • The NOMAD Laboratory at the FHI of the Max-Planck-Gesellschaft and IRIS-Adlershof of the Humboldt-Universität zu Berlin
    • The NOMAD Laboratory at the Fritz Haber Institute of the MPG
    • The NOMAD Laboratory at the FHI of the Max Planck Society

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• Electronic Structures of Ternary Compounds GeSbTe Based on Machine Learning Empirical Pseudopotentials

  ORAL

  March 5, 2024, 5:36 PM – March 5, 2024, 5:48 PM

  Presenters

  • Sungmo Kang

    • Korea Institute for Advanced Study

  Authors

  • Sungmo Kang

    • Korea Institute for Advanced Study

  • Rokyeon Kim

    • Korea Institute for Advanced Study

  • Young-Woo Son

    • Korea Institute for Advanced Study

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• Anharmonicity in cubic boron arsenide: a machine-learning based force-field study

  ORAL

  March 5, 2024, 5:48 PM – March 5, 2024, 6:00 PM

  Presenters

  • Martin Callsen

    • Institute of Atomic and Molecular Sciences, Academia Sinica

  Authors

  • Martin Callsen

    • Institute of Atomic and Molecular Sciences, Academia Sinica

  • Mei-Yin Chou

    • Institute of Atomic and Molecular Sciences, Academia Sinica
    • Academia Sinica

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