Foundational Models for Physics Research
FOCUS · MAR-J37 · ID: 3091596
Presentations
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Towards a foundation model for protein biophysics
ORAL · Invited
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Presenters
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Frank Noe
Microsoft Corporation
Authors
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Frank Noe
Microsoft Corporation
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Kin Long Kelvin Lee
ORAL · Invited
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Presenters
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Kin L Lee
Intel Labs
Authors
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Kin L Lee
Intel Labs
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Invited : Kinematic from imaging: a lesson from using machine learning on active nematics
ORAL · Invited
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Presenters
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Aparna Baskaran
Brandeis University
Authors
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Aparna Baskaran
Brandeis University
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Title: Temporal Deep Learning Architecture for Forecasting Melt Properties in Extrusion-Based Manufacturing Oral: Temporal Deep Learning Architecture for Forecasting Melt Properties in Extrusion-Based Manufacturing
ORAL
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Presenters
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Mohammad B Akram
University of New Haven
Authors
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Mohammad B Akram
University of New Haven
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Ganesh Balasubramanian
University of New Haven
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Using Bayesian Machine Learning to Extend the Range of Ab Initio Many-Body Calculations of Infinite Matter Systems
ORAL
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Publication: J. Chem. Phys. 161, 134108 (2024)<br>arXiv:2409.18234 (submitted to Physical Review C)
Presenters
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Julie L Butler
University of Mount Union
Authors
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Julie L Butler
University of Mount Union
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Christian Drischler
Ohio University
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Morten Hjorth-Jensen
Facility for Rare Isotope Beams, Michigan State University, University of Oslo, Facility for Rare Isotope Beams, Michigan State University
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Gustav R Jansen
Oak Ridge National Laboratory
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Justin G Lietz
NVIDIA Corporation
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Typical reconstruction limit and phase transition of maximum entropy method
ORAL
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Presenters
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Masaru Hitomi
Tohoku University
Authors
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Masaru Hitomi
Tohoku University
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Masayuki Ohzeki
Tohoku University
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Constructing Superconductivity and Magnetism Databases using Large Language Models
ORAL
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Publication: Planned paper:<br>Constructing Superconductivity and Magnetism Databases using Large Language Models
Presenters
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Louis D Primeau
University of Tennessee
Authors
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Louis D Primeau
University of Tennessee
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Yang Zhang
University of Tennessee
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Adrian Del Maestro
University of Tennessee, University of Tennessee-Knoxville
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Understanding What Large Language Models Are Good For
ORAL
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Presenters
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John McNally
Wolfram Research, Inc.
Authors
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John McNally
Wolfram Research, Inc.
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Mads Bahrami
Wolfram Research, Inc.
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Yi Yin
Wolfram Research, Inc.
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Michael Trott
Wolfram Research, Inc.
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