Machine Learning for Energetic Materials II
ORAL · M02 · ID: 1459415
Presentations
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Prediction of Explosive Performance and Safety Parameters Using Derivatized SMILES Data
ORAL
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Presenters
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Jack V Davis
Los Alamos National Laboratory
Authors
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Jack V Davis
Los Alamos National Laboratory
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Frank Marrs
Los Alamos National Laboratory
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Mike Grosskopf
Los Alamos National Laboratory
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Marc J Cawkwell
Los Alamos National Laboratory
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Virginia W Manner
Los Alamos Natl Lab, Los Alamos National Laboratory
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Out-of-sample Transition Temperature Predictions via Physics-informed Transfer Learning
ORAL
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Presenters
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Joshua Lansford
U.S. Army Combat Capabilities Development Command (DEVCOM) Army Research Laboratory
Authors
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Joshua Lansford
U.S. Army Combat Capabilities Development Command (DEVCOM) Army Research Laboratory
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Brian C Barnes
U.S. Army Combat Capabilities Development Command (DEVCOM) Army Research Laboratory
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Klavs F Jensen
Massachusetts Institute of Technology
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Machine learning models for energetic materials properties using multi-task learning
ORAL
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Presenters
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Robert J Appleton
Purdue University
Authors
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Robert J Appleton
Purdue University
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Daniel Klinger
Purdue University
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Brian H Lee
Purdue University
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Alejandro H Strachan
Purdue University
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Samuel Blankenship
Purdue University
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Sohee Kim
Purdue University
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Michael Taylor
New Mexico Institute of Mining and Technology
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Brian C Barnes
U.S. Army Combat Capabilities Development Command (DEVCOM) Army Research Laboratory
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Steven F Son
Purdue University
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A Data Fusion Approach for Quantitative Property Prediction Using Natural Language Processing
ORAL
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Presenters
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Allen Garcia
University of Maryland, College Park
Authors
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Allen Garcia
University of Maryland, College Park
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Zois Boukouvalas
American University
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Francis G VanGessel
Naval Surface Warfare Center, University of Maryland, College Park
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Ruth Doherty
Energetics Technology Center
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Peter W Chung
University of Maryland, College Park
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