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, 4:00 PM – March 5, 2024, 4:36 PM

  Presenters

  • Heather Kulik

    MIT

  Authors

  • Heather Kulik

    MIT

  View abstract →

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

  ORAL

  March 5, 2024, 4:36 PM – March 5, 2024, 4:48 PM

  Presenters

  • Martin Suchara

    Microsoft Corporation

  Authors

  • Martin Suchara

    Microsoft Corporation

  View abstract →

• Accelerating materials discovery using integrated deep machine learning approaches

  ORAL

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

  View abstract →

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

  ORAL

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

  View abstract →

• Incorporating explicit electrostatic interactions in machine learning potentials

  ORAL

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

  Presenters

  • Max Veit

    Aalto University

  Authors

  • Max Veit

    Aalto University

  • Miguel Caro

    Aalto University

  View abstract →

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

  ORAL

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

  View abstract →

• ML Gradients in Molecular Simulations

  ORAL · Invited

  March 5, 2024, 5:36 PM – March 5, 2024, 6:12 PM

  Presenters

  • Rafael Gomez-Bombarelli

    MIT

  Authors

  • Rafael Gomez-Bombarelli

    MIT

  View abstract →

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

  ORAL

  March 5, 2024, 6:12 PM – March 5, 2024, 6:24 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

  View abstract →

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

  ORAL

  March 5, 2024, 6:24 PM – March 5, 2024, 6:36 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

  View abstract →

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

  ORAL

  March 5, 2024, 6:36 PM – March 5, 2024, 6:48 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

  View abstract →

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

  ORAL

  March 5, 2024, 6:48 PM – March 5, 2024, 7:00 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

  View abstract →