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Discovery of New Plasmonic Metals via High-Throughput Machine Learning
Ethan P. Shapera,
André Schleife
Materials Science and Engineering
National Center for Supercomputing Applications (NCSA)
Materials Research Lab
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Keyphrases
Optical Properties
100%
High-throughput
100%
Density Functional Theory
100%
Plasmonics
100%
Machine Learning
100%
Plasmonic Metal
100%
First-principles Method
50%
Electronic Structure
50%
Optical Field
50%
Training Set
50%
Intraband Transition
50%
Material Selection
50%
Interband
50%
High Computational Cost
50%
Low Loss
50%
Computational Data
50%
Collective Oscillations
50%
Selection Problem
50%
Drude Model
50%
Online Database
50%
Chemical Formula
50%
Dmitri Mendeleev
50%
Random Forest Regressor
50%
Materials Project
50%
Engineering
Plasmonics
100%
Learning System
100%
Q Factor
50%
Nanoscale
25%
Computational Cost
25%
Optical Field
25%
Random Forest
25%
Regressors
25%
Chemical Formula
25%
Material Selection Problem
25%
Electronic State
25%
Material Science
Density
100%
Optical Property
100%
First Principle Simulation
50%
Material Selection
50%