Data Science, AI and Machine Learning in Physics I

Presentations

• Combining data, physics and machine learning for accelerating materials computations

  ORAL · Invited

  March 7, 2024, 8:00 AM – March 7, 2024, 8:36 AM

  Publication: [1] K. Bystrom, B. Kozinsky, arXiv:2303.00682 (2023)
  [2] S. Batzner et al, Nature Comm. 13 (1), 2453 (2022)
  [3] A. Musaelian, S. Batzner et al, Nature Comm. 14, 579 (2023)
  [4] J. Vandermause et al, Nature Comm. 13 (1), 5183 (2022)
  

  Presenters

  • Boris Kozinsky

    • Harvard University

  Authors

  • Boris Kozinsky

    • Harvard University

  View abstract →

• Performing Hartree-Fock many-body physics calculations with large language models

  ORAL

  March 7, 2024, 8:36 AM – March 7, 2024, 8:48 AM

  Presenters

  • Eun-Ah Kim

    • Cornell University

  Authors

  • Eun-Ah Kim

    • Cornell University

  • Haining Pan

    • Rutgers University

  • Nayantara Mudur

    • Google Research/Harvard University

  • William Taranto

    • Cornell University

  • Subhashini Venugopalan

    • Google Research

  • Yasaman Bahri

    • Google LLC

  • Michael P Brenner

    • Harvard University

  View abstract →

• Towards Open Science in Materials Synthesis and Characterization: Experiences from the 2DCC

  ORAL

  March 7, 2024, 8:48 AM – March 7, 2024, 9:00 AM

  Presenters

  • Anthony R Richardella

    • Pennsylvania State University

  Authors

  • Anthony R Richardella

    • Pennsylvania State University

  • Konrad Hilse

    • Pennsylvania State University

  • Kevin Dressler

    • Pennsylvania State University

  • Wesley F Reinhart

    • Pennsylvania State University
    • Penn State

  • Joan M Redwing

    • Pennsylvania State University

  • Nitin Samarth

    • Pennsylvania State University

  • Vincent H Crespi

    • Pennsylvania State University

  View abstract →

• Complex Langevin and machine learning approaches to the non-linear sigma model with a topological term

  ORAL

  March 7, 2024, 9:00 AM – March 7, 2024, 9:12 AM

  Presenters

  • Casey Berger

    • Smith College

  Authors

  • Casey Berger

    • Smith College

  • Adelaide Esseln

    • Smith College

  View abstract →

• Machine learning of quantum walk with classical randomness

  ORAL

  March 7, 2024, 9:12 AM – March 7, 2024, 9:24 AM

  Publication: Phys. Rev. E 108, 035308 (2023)

  Presenters

  • Christopher Mastandrea

    • University of California, Merced

  Authors

  • Christopher Mastandrea

    • University of California, Merced

  • Chih-Chun Chien

    • University of California, Merced

  View abstract →

• Machine Learning Discovery of a New Descriptor for Topological Semimetal

  ORAL

  March 7, 2024, 9:24 AM – March 7, 2024, 9:36 AM

  Presenters

  • YANJUN LIU

    • Cornell University

  Authors

  • YANJUN LIU

    • Cornell University

  • Krishnanand M Mallayya

    • Cornell University

  • Milena Jovanovic

    • Princeton University

  • Wesley J Maddox

    • Jump Trading LLC

  • Andrew G Wilson

    • New York University

  • Sebastian Klemenz

    • Fraunhofer IWKS

  • Leslie M Schoop

    • Princeton University

  • Eun-Ah Kim

    • Cornell University

  View abstract →

• Variational formulation of physics-informed neural networks

  ORAL

  March 7, 2024, 9:36 AM – March 7, 2024, 9:48 AM

  Presenters

  • Chinmay Katke

    • Virginia Tech

  Authors

  • Chinmay Katke

    • Virginia Tech

  • C. Nadir Kaplan

    • Virginia Tech

  View abstract →

• A data-driven framework for non-stationary complex systems: Blending generalized Langevin and neural ordinary-differential equations.

  ORAL

  March 7, 2024, 9:48 AM – March 7, 2024, 10:00 AM

  Publication: Manuscript submitted to Chaos journal focus issue: Data-Driven Models and Analysis of Complex Systems.

  Presenters

  • Antonio Malpica-Morales

    • Imperial College London

  Authors

  • Antonio Malpica-Morales

    • Imperial College London

  • Serafim Kalliadasis

    • Imperial College London

  • Miguel A Duran-Olivencia

    • Imperial College London

  View abstract →

• Machine Learning for Adsorption Processes

  ORAL · Invited

  March 7, 2024, 10:00 AM – March 7, 2024, 10:36 AM

  Publication: Y.Z.S. Sun, R.F. DeJaco, and J.I. Siepmann, 'Deep neural network learning of complex binary sorption equilibria from molecular simulation data,' Chem. Sci. 10, 4377–4388 (2019).
  K. Shi, Z. Li, D.M. Anstine, D. Tang, C.M. Colina, D.S. Sholl, J.I. Siepmann, and R.Q. Snurr, 'Two-dimensional energy histograms as features for machine learning to predict adsorption in diverse nanoporous materials,' J. Chem. Theory Comput. 23, 4568–4583 (2023).

  Presenters

  • J. Ilja Siepmann

    • University of Minnesota

  Authors

  • J. Ilja Siepmann

    • University of Minnesota

  View abstract →

• Optimization of physical quantities in the autoencoder latent space

  ORAL

  March 7, 2024, 10:36 AM – March 7, 2024, 10:48 AM

  Publication: Park, S.M., Yoon, H.G., Lee, D.B. et al. Optimization of physical quantities in the autoencoder latent space. Sci Rep 12, 9003 (2022). https://doi.org/10.1038/s41598-022-13007-5

  Presenters

  • Seong Min Park

    • Kyung Hee University
    • KyungHee University

  Authors

  • Seong Min Park

    • Kyung Hee University
    • KyungHee University

  • Changyeon Won

    • Kyung Hee University
    • KyungHee University

  • Han Gyu Yoon

    • Kyung Hee university
    • KyungHee University
    • Kyung Hee University

  • Doo Bong Lee

    • Kyung Hee University
    • KyungHee University

  • Jun Woo Choi

    • Korea Institute of Science and Technology
    • Korea Institute of science and technology
    • KIST

  • Hee Young Kwon

    • Korea Institute of Science and Technology
    • Korea Institute of science and technology

  View abstract →

• Crystal structure generative modeling based on diffusion probabilistic models and variational autoencoder

  ORAL

  March 7, 2024, 10:48 AM – March 7, 2024, 11:00 AM

  Publication: Teerachote Pakornchote, Natthaphon Choomphon-anomakhun, Sorrjit Arrerut, Chayanon Atthapak, Sakarn Khamkaeo, Thiparat Chotibut, and Thiti Bovornratanaraks, "Diffusion probabilistic models enhance variational autoencoder for crystal structure generative modeling", arXiv:2308.02165.

  Presenters

  • Teerachote Pakornchote

    • Chulalongkorn University

  Authors

  • Teerachote Pakornchote

    • Chulalongkorn University

  • Natthaphon Choomphon-anomakhun

    • Chulalongkorn University

  • Sorrjit Arrerut

    • Chulalongkorn University

  • Chayanon Atthapak

    • Chulalongkorn University

  • Sakarn Khamkaeo

    • Chulalongkorn University

  • Thiparat Chotibut

    • Chulalongkorn University

  • Thiti Bovornratanaraks

    • Chulalongkorn University

  View abstract →