Machine Learning for Materials Science I

Presentations

• Predicting aggregate morphology of sequence-defined macromolecules with recurrent neural networks

  ORAL · Invited

  March 4, 2024, 9:00 AM – March 4, 2024, 9:36 AM

  Presenters

  • Antonia Statt

    University of Illinois at Urbana-Champaign

  Authors

  • Antonia Statt

    University of Illinois at Urbana-Champaign

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• Active Learning for Discovering Complex Phase Diagrams with Gaussian Processes

  ORAL

  March 4, 2024, 9:36 AM – March 4, 2024, 9:48 AM

  Presenters

  • Chunjing Jia

    University of Florida

  Authors

  • Chunjing Jia

    University of Florida

  • Max Zhu

    University of Cambridge

  • Jian Yao

    Southern University of Science and Technology

  • Marcus Mynatt

    University of Florida

  • Hubert Pugzlys

    University of Florida

  • Shuyi Li

    University of Florida

  • Sergio Bacallado

    University of Cambridge

  • Qingyuan Zhao

    University of Cambridge

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• Data-driven studies of two-dimensional materials and their nonlinear optical properties

  ORAL

  March 4, 2024, 9:48 AM – March 4, 2024, 10:00 AM

  Presenters

  • Kai Wagoner-Oshima

    Rensselear Polytechnic Institute

  Authors

  • Kai Wagoner-Oshima

    Rensselear Polytechnic Institute

  • Romakanta Bhattarai

    Rensselaer Polytechnic Institute

  • Trevor David Rhone

    Rensselaer Polytechnic Institute

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• Using Data to Enhance Mechanistic Modeling of Microstructure Evolution in Silicon

  ORAL · Invited

  March 4, 2024, 10:00 AM – March 4, 2024, 10:36 AM

  Publication: [1] Y. Yang, A. Sattler, and T. Sinno, Data-Assisted Physical Modeling of Oxygen Precipitation in Silicon Wafers, J. Appl. Phys. 125 (2019), 165705.
  [2] C. Y. Chuang, S. M. Han, L. A. Zepeda-Ruiz, and T. Sinno, On Coarse Projective Integration for Atomic Deposition in Amorphous Systems, J. Chem. Phys. 143 (2015) 134703.
  [3] C. Y. Chuang, Q. Li, D. Leonhardt, S. M. Han, and T. Sinno, Atomistic Analysis of Germanium on Amorphous SiO2 using an Empirical Interatomic Potential, Surf. Sci. 609 (2013) 221.

  Presenters

  • Talid Sinno

    University of Pennsylvania

  Authors

  • Talid Sinno

    University of Pennsylvania

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• Data-Driven Models for Predicting Stability of Electrocatalysts in Aqueous Environments

  ORAL

  March 4, 2024, 10:36 AM – March 4, 2024, 10:48 AM

  Presenters

  • Seda Oturak

    Pennsylvania State University, The Pennsylvania State University

  Authors

  • Seda Oturak

    Pennsylvania State University, The Pennsylvania State University

  • Ismaila Dabo

    Pennsylvania State University, The Pennsylvania State University

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• ChemChat | Conversational Expert Assistant in Material Science and Data Visualization

  ORAL

  March 4, 2024, 10:48 AM – March 4, 2024, 11:00 AM

  Publication: 1) Accepted Book Chapter: Language models in molecular discovery (https://arxiv.org/abs/2309.16235)
  2) Submitted to NeurIPS 2023 Workshop AI4Science under Attention Track and abstract #64: Large Language Models in Molecular Discovery

  Presenters

  • Tim Erdmann

    IBM Research - Almaden

  Authors

  • Tim Erdmann

    IBM Research - Almaden

  • Sarathkrishna Swaminathan

    IBM Research - Almaden

  • Stefan Zecevic

    IBM Research - Almaden

  • Brandi Ransom

    IBM Research - Almaden

  • Nathan Park

    IBM Research - Almaden

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• Towards a Multi-Objective Optimization of Subgroups for the Discovery of Materials with Exceptional Performance

  ORAL

  March 4, 2024, 11:00 AM – March 4, 2024, 11:12 AM

  Presenters

  • Lucas Foppa

    Fritz Haber Institute of the Max Planck Society

  Authors

  • Lucas Foppa

    Fritz Haber Institute 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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• Equivarient Electron Density Predictions Accelerate Density Functional Theory Calculations

  ORAL

  March 4, 2024, 11:12 AM – March 4, 2024, 11:24 AM

  Presenters

  • Eric Taw

    MIT Lincoln Laboratory

  Authors

  • Thomas Koker

    MIT Lincoln Laboratory

  • Keegan Quigley

    MIT Lincoln Laboratory

  • Eric Taw

    MIT Lincoln Laboratory

  • Lin Li

    MIT Lincoln Laboratory

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• Generative neural networks for synthetic PBX microstructures with varying levels of damage to evaluate shock sensitivity through meso-scale simulations

  ORAL

  March 4, 2024, 11:24 AM – March 4, 2024, 11:36 AM

  Presenters

  • Irene Fang

    University of Iowa

  Authors

  • Irene Fang

    University of Iowa

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• Recent Advancements in SISSO as Applied to Thermal Conductivity

  ORAL

  March 4, 2024, 11:36 AM – March 4, 2024, 11:48 AM

  Publication: T. A. R. Purcell et al. J. Chem. Phys. 159, 114110 (2023)
  T. A. R. Purcell et al. npj Comput. Mater. 9, 112 (2023)

  Presenters

  • Thomas A Purcell

    The NOMAD Laboratory at the FHI of the MPG, The University of Arizona

  Authors

  • Thomas A Purcell

    The NOMAD Laboratory at the FHI of the MPG, The University of Arizona

  • 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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• Closed-Loop Control of Non-Newtonian Fluid Flow Using Machine Learning

  ORAL

  March 4, 2024, 11:48 AM – March 4, 2024, 12:00 PM

  Presenters

  • Xin Zhang

    University of Massachusetts Amherst

  Authors

  • Xin Zhang

    University of Massachusetts Amherst

  • Huilu Bao

    University of Massachusetts Amherst

  • Xiaoyu Zhang

    University of Massachusetts Amherst

  • Xiao Fan

    University of Massachusetts Amherst

  • Jinglei Ping

    University of Massachusetts Amherst

  View abstract →