Physical Applications of Machine-Learning for Atomistic Simulations

Presentations

• Generative design of molecules and materials

  ORAL · Invited

  March 17, 2026, 8:00 AM – March 17, 2026, 8:36 AM

  Presenters

  • Stefano Martiniani

    • New York University (NYU)

  Authors

  • Stefano Martiniani

    • New York University (NYU)

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• Machine-Learning Interatomic Potentials for Radiation Damage Analysis in High-Temperature Superconductors

  ORAL

  March 17, 2026, 8:36 AM – March 17, 2026, 8:48 AM

  Publication: 1) D. Gambino, N. D. Eugenio et al., "The diffusion-driven orthorhombic to tetragonal transition in YBa₂Cu₃O₇ derived with a machine learning interatomic potential," (2025), arXiv:2509.26095
  2) N. D. Eugenio et al., "Benchmarking Machine-Learned Interatomic Potentials for Structural and Defect Properties of YBa2Cu3O7−δ", in preparation
  3) A. Dickson, N. D. Eugenio et al., "Evaluating Machine Learned Interatomic Potentials for Radiation Damage Simulations in YBa2Cu3O7−δ", in preparation

  Presenters

  • Niccolò Di Eugenio

    • Department of Applied Science and Technology, Politecnico di Torino, I-10129 Torino, Italy; INFN, Sezione di Torino, I-10125 Torino, Italy

  Authors

  • Niccolò Di Eugenio

    • Department of Applied Science and Technology, Politecnico di Torino, I-10129 Torino, Italy; INFN, Sezione di Torino, I-10125 Torino, Italy

  • Ashley Dickson

    • School of Engineering, Lancaster University, Lancaster, LA1 4YW, United Kingdom

  • Federico Ledda

    • Department of Applied Science and Technology, Politecnico di Torino, I-10129 Torino, Italy; INFN, Sezione di Torino, I-10125 Torino, Italy; Dep

  • Daniele Torsello

    • Politecnico di Torino

  • Francesco Laviano

    • Politecnico di Torino

  • Samuel Murphy

    • School of Engineering, Lancaster University, Lancaster, LA1 4YW, United Kingdom

  • Flyura Djurabekova

    • Department of Physics, University of Helsinki, P.O. Box 43, FI-00014, Finland; Helsinki Institute of Physics, University of Helsinki, P.O. Box 43, FI-00014, Finland

  • Antonio Trotta

    • MAFE, Eni S.p.A., Venice, Italy

  • Mark R. Gilbert

    • United Kingdom Atomic Energy Authority, Culham Campus, Abingdon, OX14 3DB, United Kingdom

  • Duc Nguyen-Manh

    • United Kingdom Atomic Energy Authority, Culham Campus, Abingdon, OX14 3DB, United Kingdom

  • Erik Gallo

    • MAFE, Eni S.p.A., Venice, Italy

  • Davide Gambino

    • Linkoping University

  View abstract →

• Abstract Withdrawn

  ORAL · Withdrawn

  March 17, 2026, 8:48 AM – March 17, 2026, 9:00 AM

  View abstract →

• Machine-Learning Interatomic Potentials for Twisted Moiré Materials

  ORAL

  March 17, 2026, 9:00 AM – March 17, 2026, 9:12 AM

  Presenters

  • Thomas Huang

    • University of Washington

  Authors

  • Thomas Huang

    • University of Washington

  • Yueyao Fan

    • University of Washington

  • Kaichen Xie

    • University of Washington

  • Eric J Bylaska

    • PNNL/Chemical Physics Theory Team

  • Jenna A Bilbrey

    • PNNL
    • Pacific Northwest National Laboratory (PNNL)

  • Peter V Sushko

    • Pacific Northwest National Laboratory (PNNL)

  • Di Xiao

    • University of Washington

  • Ting Cao

    • University of Washington

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• Design and Modeling of real-world 2D Materials and Interfaces for AI applications with "Mat3ra-2D"

  ORAL

  March 17, 2026, 9:12 AM – March 17, 2026, 9:24 AM

  Presenters

  • Vsevolod (Seth) Biryukov

    • Exabyte Inc.

  Authors

  • Vsevolod (Seth) Biryukov

    • Exabyte Inc.

  • Timur Bazhirov

    • University of California, Berkeley

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• Predicting facet properties of Sn-based perovskites using machine learning interatomic potentials and active learning schemes

  ORAL

  March 17, 2026, 9:24 AM – March 17, 2026, 9:36 AM

  Presenters

  • Christopher Fivecoat

    • University of North Carolina at Chapel Hill

  Authors

  • Christopher Fivecoat

    • University of North Carolina at Chapel Hill

  • Mengen Wang

    • University of North Carolina at Chapel Hill

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• Revisiting temperature induced metallicity of the Si(001) Surface: insights from molecular dynamics simulations with machine learned interatomic potentials

  ORAL

  March 17, 2026, 9:36 AM – March 17, 2026, 9:48 AM

  Presenters

  • John W Janisch

    • University of Central Florida

  Authors

  • John W Janisch

    • University of Central Florida

  • Sonali Joshi

    • University of Illinois at Urbana-Champaign

  • Duy Le

    • University of Central Florida

  • Talat Shahnaz Rahman

    • University of Central Florida

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• Predicting incoherent interface structures in Cu–Ni–Si-Mn alloys using machine-learning interatomic potentials

  ORAL

  March 17, 2026, 9:48 AM – March 17, 2026, 10:00 AM

  Publication: [1] S. Z. Han, I.-S. Jeong, B. Ryu, S. J. Lee, J. H. Ahn, and E.-A. Choi, Enhanced strength of Cu-Ni-Si alloy via heterogeneous nucleation at grain boundaries during homogenization, Materials Characterization 215, 114198 (2024).

  Presenters

  • Byungki Ryu

    • Korea Electrotechnology Resesearch Institute

  Authors

  • Byungki Ryu

    • Korea Electrotechnology Resesearch Institute

  • Il-Seok Jeong

    • Korea Institute of Materials Science

  • Seung Zeon Han

    • Korea Institute of Materials Science

  • Eun-Ae Choi

    • Korea Institute of Materials Science

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• Active learning using uncertainty-driven dynamics for configurational space search of molecules on metal surfaces

  ORAL

  March 17, 2026, 10:00 AM – March 17, 2026, 10:12 AM

  Presenters

  • Moin Uddin Maruf

    • Texas Tech University

  Authors

  • Moin Uddin Maruf

    • Texas Tech University

  • Zeeshan Ahmad

    • Texas Tech University

  • Sungmin Kim

    • Samsung Advanced Institute of Technology

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• Water Auto-ionization Pathways at Graphene Interfaces via Enhanced Sampling and Machine Learning

  ORAL

  March 17, 2026, 10:12 AM – March 17, 2026, 10:24 AM

  Presenters

  • Zhiying Yi

    • University of Chicago

  Authors

  • Zhiying Yi

    • University of Chicago

  • Yinan Xu

    • New York University

  • Yezhi Jin

    • University of Chicago

  • Pablo Zubieta

    • University of Chicago

  • Joan Manuel Montes de Oca

    • Argonne National Laboratory

  • Paul F Nealey

    • University of Chicago

  • Juan de Pablo

    • New York University
    • NYU

  View abstract →