Physical Applications of Machine-Learning for Atomistic Simulations

Presentations

• Generative design of molecules and materials

  Invited-In-person · 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-In-person

  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

    • Politecnico di Torino

  Authors

  • Niccolò Di Eugenio

    • Politecnico di Torino

  • Ashley Dickson

  • Federico Ledda

  • Daniele Torsello

    • Politecnico di Torino

  • Francesco Laviano

  • Samuel Murphy

  • Flyura Djurabekova

  • Antonio Trotta

  • Mark R. Gilbert

  • Duc Nguyen-Manh

  • Erik Gallo

  • Davide Gambino

    • Linkoping University

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• Atomistic Growth Simulation and AI‑Driven Structure Generation of Amorphous Carbon

  Oral-In-person · Withdrawn

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

  Publication: 0. Tian et al.,Disorder-tuned conductivity in amorphous monolayer carbon, Nature 2023,615,56–61.
  1. J. Hu et al, Simulated Atomistic Growth of Amorphous Monolayer Carbon, J. Phys. Chem. Lett. 16, 6866−6873 (2025).
  2. S. Liu,‡ Ran Cao,‡ J. Hu‡ et al, Degree of disorder-regulated ion transport through amorphous monolayer carbon, RSC Adv., 14, 17032 (2024).
  3. J. Hu et al, Learning Rare‑Event Growth: A 3D MACE‑Based Interatomic Potential for Two-dimensional Amorphous Carbon (planned papers)
  4. J. Hu et al, Generative Inverse Design of Carbon Networks (planned papers)

  Presenters

  • Jiani Hu

    • Peking Univeristy

  Authors

  • Jiani Hu

    • Peking Univeristy

  • Mouyang Cheng

    • Massachusetts Institute of Technology

  • Ji Chen

    • Peking Univ

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• Machine-Learning Interatomic Potentials for Twisted Moiré Materials

  Oral-In-person

  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 Bylaska

    • PNNL/Chemical Physics Theory Team

  • Jenna Bilbrey

  • Peter 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-In-person

  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-In-person

  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

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

  Oral-In-person

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

  Presenters

  • John Janisch

    • University of Central Florida

  Authors

  • John Janisch

    • University of Central Florida

  • Sonali Joshi

    • University of Illinois at Urbana-Champaign

  • Duy Le

    • University of Central Florida

  • Talat 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-In-person

  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

  • Seung Zeon Han

  • Eun-Ae Choi

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

  Oral-In-person

  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

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

  Oral-In-person

  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

  • Yezhi Jin

    • University of Chicago

  • Pablo Zubieta

    • University of Chicago

  • Joan Montes de Oca

    • Argonne National Laboratory

  • Paul Nealey

    • University of Chicago

  • Juan de Pablo

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