Machine Learning Potentials, Foundation Models, Molecular Dynamics and Monte Carlo for Materials Research - I
FOCUS · MAR-B46 · ID: MAR-B46
Presentations
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The many roles that AI can play in accelerating scientific discovery
Invited-In-person · Invited · Withdrawn
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Presenters
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Rick Stevens
- Argonne National Laboratory
Authors
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Rick Stevens
- Argonne National Laboratory
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Mixture of Experts for Interatomic Potentials in the NequIP and Allegro Framework
Oral-In-person
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Presenters
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Gabriel de Miranda Nascimento
- Massachusetts Institute of Technology
Authors
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Gabriel de Miranda Nascimento
- Massachusetts Institute of Technology
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Marc Descoteaux
- Harvard University
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Laura Zichi
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Chuin Wei Tan
- Harvard University
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Seán Kavanagh
- University of Cambridge
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William Witt
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Boris Kozinsky
- Harvard University
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LAMMPS at exascale
Oral-In-person
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Publication: https://arxiv.org/abs/2508.13523
Presenters
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Anders Johansson
- Sandia National Labs
Authors
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Anders Johansson
- Sandia National Labs
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Excited State Machine Learning Molecular Dynamics Simulations for Ultrafast Scattering Experiments.
Oral-In-person · Withdrawn
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Presenters
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Thomas Linker
- SLAC National Accelerator Laboratory
Authors
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Thomas Linker
- SLAC National Accelerator Laboratory
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Deep Generative AI and Liquid AI Predictive Models: Applications in Materials, Energy, and Health Sciences Rajiv Kalia, Yash Gandhi, Aiichiro Nakano and Priya Vashishta University of Southern California, Los Angeles, CA 90089
Invited-In-person · Invited
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Presenters
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Rajiv Kalia
- University of Southern California
Authors
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Rajiv Kalia
- University of Southern California
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Performance of universal machine learning potentials in global optimization
Oral-In-person
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Presenters
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Edan Marcial
- Binghamton University
Authors
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Edan Marcial
- Binghamton University
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Laxman Chaudhray
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Alexey Kolmogorov
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Olesya Gorbunova
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QERNEL: Quantum Expert-Routed Neural Learner
Oral-In-person
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Publication: [1] M. Geier, Kh. Nazaryan, T. Zaklama, and L. Fu, "Self-attention neural network for solving correlated electron problems in solids," Phys. Rev. B, vol. 112, no. 4, p. 045119, Jul. 2025.
[2] Kh. Nazaryan, F. Gaggioli, Y. Teng, and L. Fu, "Artificial Intelligence for Quantum Matter: Finding a Needle in a Haystack," arXiv preprint arXiv:2507.13322, 2025, doi:10.48550/arXiv.2507.13322
[3] I. von Glehn, J. S. Spencer, and D. Pfau, "A Self-Attention Ansatz for Ab-initio Quantum Chemistry," arXiv preprint arXiv:2211.13672, 2022, doi:10.48550/arXiv.2211.13672Presenters
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Khachatur Nazaryan
- Massachusetts Institute of Technology
Authors
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Khachatur Nazaryan
- Massachusetts Institute of Technology
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Liang Fu
- Massachusetts Institute of Technology
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Multiscale Light-Matter Dynamics in Quantum Materials: From Electrons to Topological Superlattices
Oral-In-person
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Presenters
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Taufeq Mohammed Razakh
- University of Southern California
Authors
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Taufeq Mohammed Razakh
- University of Southern California
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Ken-ichi Nomura
- University of Southern California
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Aiichiro Nakano
- University of Southern California
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Priya Vashishta
- University of Southern California
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Thomas Linker
- SLAC National Accelerator Laboratory
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Ye Luo
- Argonne National Laboratory
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Rajiv Kalia
- University of Southern California
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Developing Neural Network Machine Learning Interatomic Potentials for Molecular Dynamics Simulations of Complex Ternary Materials
Oral-In-person · Withdrawn
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Publication: J. Chem. Phys. 163, 084109 (2025)
Presenters
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Cai-Zhuang Wang
- Iowa State University
Authors
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Cai-Zhuang Wang
- Iowa State University
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Ling Tang
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Weiyi Xia
- Ames National Laboratory
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Gayatri Viswanathan
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Ernesto Soto
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Kirill Kovnir
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exa-AMD: An Exascale-Ready Framework for Accelerating the Discovery and Design of Functional Materials
Oral-In-person
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Presenters
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Weiyi Xia
- Ames National Laboratory
Authors
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Weiyi Xia
- Ames National Laboratory
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Maxim Moraru
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Ying Wai Li
- Los Alamos National Laboratory
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Cai-Zhuang Wang
- Iowa State University
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