Material Science and Machine Learning III
FOCUS · Z32 · ID: 48677
Presentations
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The interplay between quantum computing and reinforcement learning
ORAL · Invited
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Presenters
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Vedran Dunjko
- Leiden University
Authors
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Vedran Dunjko
- Leiden University
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Development of machine learning framework to fit quantum-mechanical ab-initio potential energy surface to a cite-cite molecular potential
ORAL
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Presenters
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Bhanuday Sharma
- Indian Institute of Technology Kanpur
Authors
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Bhanuday Sharma
- Indian Institute of Technology Kanpur
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Savitha Pareek
- DELL HPC and AI Innovation Lab, Bengaluru, India
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Ashish K Singh
- DELL HPC and AI Innovation Lab, Bengaluru, India
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Rakesh Kumar
- Indian Institute of Technology Kanpur, India
- Indian Institute of Technology Kanpur
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Machine learning the saling property of density functionals via data augmentation
ORAL
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Presenters
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Weiyi Gong
- Temple University
Authors
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Weiyi Gong
- Temple University
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Tao Sun
- Stony Brook University
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Peng Chu
- Temple University
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Hexin Bai
- Temple University
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Anoj Aryal
- Temple University
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Shah Tanvir-Ur-Rahman Chowdhury
- Temple University
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Jie Yu
- Temple University
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Haibin Ling
- Stony Brook University
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John P Perdew
- Temple University
- Departments of Physics and Chemistry, Temple U., Philadelphia, PA 19122
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Qimin Yan
- Temple University
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Benchmarking Descriptors, Models, and Systems for Many-Body Machine Learned Force Fields in Molten Transition Metals
ORAL
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Presenters
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Cameron J Owen
- Harvard University
Authors
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Cameron J Owen
- Harvard University
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Steven B Torrisi
- Harvard University, Toyota Research Institute
- Harvard University
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Isabel Diersen
- Harvard University
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Lixin Sun
- Harvard University
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Jin Soo Lim
- Harvard University
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Yu Xie
- Harvard University
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Jonathan P Vandermause
- Harvard University
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Boris Kozinsky
- Harvard University
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Zirconium Machine Learned Potential Trained on a Euclidean Neural Network
ORAL
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Presenters
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Vanessa J Meraz
- University of Texas at El Paso
Authors
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Vanessa J Meraz
- University of Texas at El Paso
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Sofia G Gomez
- University of Texas at El Paso
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Valeria I Arteaga Muniz
- University of Texas at El Paso
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Adrian De la Rocha Galán
- University of Texas at El Paso
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Tess E Smidt
- Massachusetts Institute of Technology
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Sara Kadkhodaei
- University of Illinois at Chicago
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Bert A de Jong
- Lawrence Berkeley National Laboratory
- LBNL
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Jorge A Munoz
- University of Texas at El Paso
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A machine learning-based interatomicpotential for Fe using marginalized graph kernels
ORAL
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Presenters
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Valeria I Arteaga Muniz
- University of Texas at El Paso
Authors
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Valeria I Arteaga Muniz
- University of Texas at El Paso
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Adrian De la Rocha Galán
- University of Texas at El Paso
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Vanessa J Meraz
- University of Texas at El Paso
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Yu-Hang Tang
- Lawrence Berkeley National Laboratory
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Ramon J Ravelo
- University of Texas at El Paso
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Bert A de Jong
- Lawrence Berkeley National Laboratory
- LBNL
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Jorge A Munoz
- University of Texas at El Paso
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TitleOptimization of prediction model for elastic constants of high entropy alloys by using LIDG method
ORAL
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Presenters
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Genta Hayashi
- Osaka Univ.
Authors
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Genta Hayashi
- Osaka Univ.
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Katsuhiro Suzuki
- Osaka Univ.
- Osaka University
- Division of Materials and Manufacturing Science, Osaka University
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Tomoyuki Terai
- Osaka Univ.
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Kazunori Sato
- Osaka Univ.
- Osaka University
- Division of Materials and Manufacturing Science, Osaka University
- Osaka University, CSRN-Osaka
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Building Chemical Property Models for Energetic Materials from Small Datasets using a Transfer Learning Approach
ORAL
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Presenters
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Brian C Barnes
- DEVCOM Army Research Laboratory
- US Army Research Lab Aberdeen
Authors
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Brian C Barnes
- DEVCOM Army Research Laboratory
- US Army Research Lab Aberdeen
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Joshua L Lansford
- DEVCOM Army Research Laboratory
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Betsy M Rice
- DEVCOM Army Research Laboratory
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Klavs F Jensen
- Massachusetts Institute of Technology
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Deep Learning Analysis of Polaritonic Wave Images
ORAL
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Presenters
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Suheng Xu
- Columbia University
Authors
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Suheng Xu
- Columbia University
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Alexander S McLeod
- Columbia Univ
- Columbia University
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Xinzhong Chen
- Stony Brook University (SUNY)
- State Univ of NY - Stony Brook
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Daniel J Rizzo
- Columbia University
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Bjarke S Jessen
- Columbia University
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Ziheng Yao
- State Univ of NY - Stony Brook
- Stony Brook University (SUNY)
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Zhicai Wang
- State Univ of NY - Stony Brook
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Zhiyuan Sun
- Columbia Univ
- Harvard University
- Columbia University
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Sara Shabani
- Columbia University
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Abhay N Pasupathy
- Columbia University
- Brookhaven National Laboratory & Columbia University
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Andrew J Millis
- Columbia University
- Columbia University; Flatiron Institute
- Columbia University, Flatiron Institute
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Cory R Dean
- Columbia University
- Columbia Univ
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James C Hone
- Columbia University
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Mengkun Liu
- State Univ of NY - Stony Brook
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Dmitri N Basov
- Columbia University
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Thermal Transport with Message Passing Neural Networks via the Green-Kubo Method
ORAL
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Publication: M.F. Langer, F. Knoop, C. Carbogno, M. Scheffler, and M. Rupp, in preparation
Presenters
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Marcel F Langer
- Machine Learning Group, Technische Universität Berlin and NOMAD Laboratory, Fritz Haber Institute of the Max Planck Society
Authors
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Marcel F Langer
- Machine Learning Group, Technische Universität Berlin and NOMAD Laboratory, Fritz Haber Institute of the Max Planck Society
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Florian Knoop
- NOMAD Laboratory, Fritz Haber Institute of the Max Planck Society
- The NOMAD Laboratory at the Fritz Haber Institute of the MPG
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Christian Carbogno
- NOMAD Laboratory, Fritz Haber Institute of the Max Planck Society
- Fritz-Haber-Institute
- The NOMAD Laboratory at the Fritz Haber Institute of the MPG
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Matthias Scheffler
- NOMAD Laboratory, Fritz Haber Institute of the Max Planck Society
- Fritz-Haber Institute
- The NOMAD Laboratory at the Fritz Haber Institute of the MPG
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Matthias Rupp
- Department of Computer and Information Science, University of Konstanz and NOMAD Laboratory, Fritz Haber Institute of the Max Planck Society
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Reducing Optimal Training Set Design with Many-Body Repulsive Potentials for High Accuracy Density-Functional Tight Binding Models
ORAL
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Presenters
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Huy Pham
- Lawrence Livermore Natl Lab
Authors
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Huy Pham
- Lawrence Livermore Natl Lab
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Rebecca K Lindsey
- Lawrence Livermore Natl Lab
- Lawrence Livermore National Laboratory
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Laurence E Fried
- Lawrence Livermore Natl Lab
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Nir Goldman
- Lawrence Livermore Natl Lab
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Modeling of Lithium Dendrite Growth in Ionic Liquids with Lattice Monte Carlo Simulation Method and Deep Neural Networks
ORAL
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Presenters
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Tong Gao
- Michigan Technological University
Authors
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Tong Gao
- Michigan Technological University
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Issei Nakamura
- Michigan Technological University
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