Generative Models and Machine Learning in Chemical Physics III
FOCUS · MAR-W68 · ID: 3087978
Presentations
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Building Physics into Electronic Structure Models for Coarse-Grained Molecular Representations
ORAL · Invited
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Publication: Maier, J.C.; Jackson, N.E. J. Chem. Phys. 157, 174102 (2022)<br>Maier, J.C.; Wang, C.-I.; Jackson, N.E. J. Chem. Phys. 160, 024109 (2024)<br>Wang, C.-I.; Maier, J.C.; Jackson, N.E. Chem. Sci. 2024, 15, 8390-8403.<br>Maier, J.C.; Jackson, N.E. In Preparation
Presenters
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Nicholas E Jackson
University of Illinois at Urbana-Champaign
Authors
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Nicholas E Jackson
University of Illinois at Urbana-Champaign
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Mapping phase diagrams with deep generative models
ORAL · Invited
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Publication: https://dx.doi.org/10.1088/2632-2153/ad849d
Presenters
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Jutta Rogal
New York University
Authors
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Jutta Rogal
New York University
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The performance of a Jastrow factor utilizing the Spectral Neighbor Analysis Potential descriptor for atoms, molecules, and solids.
ORAL
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Presenters
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Amanda Elizabeth Dumi
Sandia National Laboratories
Authors
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Amanda Elizabeth Dumi
Sandia National Laboratories
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Raymond C Clay
Sandia National Laboratories
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Luke Shulenburger
Sandia National Laboratories
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Machine Learning to Optimize Electrocatalysis in the Oxygen Evolution Reaction
ORAL
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Presenters
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Ethan Cichon
New Mexico State University
Authors
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Ethan Cichon
New Mexico State University
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Scott Folkman
New Mexico State University
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Rational Computational Design of Next-Generation Semiconductors
ORAL
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Publication: [1] A. Mannodi-Kanakkithodi, Comput. Mater. Sci. 243, 113108 (2024).<br>[2] J. Yang et al., J. Chem. Phys. 160, 064114 (2024).<br>[3] M.H. Rahman et al., "High-Throughput Screening of Ternary and Quaternary Chalcogenide Semiconductors for Photovoltaics", under review.<br>[4] M.H. Rahman et al., APL Machine Learning. 2, 016122 (2024).
Presenters
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Arun Kumar Mannodi Kanakkithodi
Purdue University
Authors
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Arun Kumar Mannodi Kanakkithodi
Purdue University
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Abstract Withdrawn
ORAL Withdrawn
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EGAT: A Graph Attention Network for Your Chemical Property Prediction Needs
ORAL
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Publication: • Vaddadi, S.M.; Zhao, Q.; Savoie, B.M. Graph to Activation Energy Models Easily Reach Irreducible Errors but Show Limited Transferability, J. Phys. Chem. A 128, 13, (2024)<br>• Zhao,Q.; Vaddadi, S.M.; Woulfe, M.; Ogunfowora, L.A.; Garimella, S.; Isayev, O.; Savoie, B.M. Comprehensive exploration of graphically defined reaction spaces Nat. Sci. Data 10, 145 (2023)
Presenters
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Sai Mahit Vaddadi
Purdue University
Authors
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Sai Mahit Vaddadi
Purdue University
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Brett M Savoie
Purdue University
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Machine Learning Optimization of Laser-Induced Graphene Parameters for Surface-Enhanced Raman Spectroscopic Detection of Glucose
ORAL
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Presenters
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Govinda Ghimire
Iowa State University
Authors
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Govinda Ghimire
Iowa State University
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Patrick A Johnson
Iowa State University
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Farshid Noormohammadi
Iowa State University
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Machine Learning-Assisted Identification of Superconducting Bilayer Nickelate Thin Films via X-Ray Diffraction Analysis
ORAL
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Publication: [1] E.K. Ko et al. Signatures of ambient pressure superconductivity in thin film La3Ni2O7. Submitted.
Presenters
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Yaoju Tarn
Stanford University
Authors
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Yaoju Tarn
Stanford University
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Yidi Liu
Stanford University
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Eun-Kyo Ko
Stanford University
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Yijun Yu
Stanford University
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Harold Y Hwang
Stanford University
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Diffusion Models for Conditional Generation of Hypothetical New Families of Superconductors
ORAL
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Publication: Yuan, S., Dordevic, S.V. Diffusion models for conditional generation of hypothetical new families of superconductors. Sci Rep 14, 10275 (2024). https://doi.org/10.1038/s41598-024-61040-3
Presenters
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Samuel Yuan
Homestead High School
Authors
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Samuel Yuan
Homestead High School
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Sasa V Dordevic
University of Akron
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