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A data-driven method for optimization of classical interatomic potentials
Benjamin A. Jasperson
,
Harley T. Johnson
Mechanical Science and Engineering
Office of the Vice Chancellor for Research and Innovation
Materials Science and Engineering
Materials Research Lab
National Center for Supercomputing Applications (NCSA)
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Keyphrases
Optimization Algorithm
100%
Data-driven Methods
100%
Classical Interatomic Potential
100%
Material Properties
66%
Density Functional Theory
66%
Atomistic Simulation
66%
Interatomic Potential
66%
Process Optimization
33%
Potential Parameters
33%
First-principles
33%
Parameter Values
33%
Scaling Properties
33%
Fitting Process
33%
Training Data
33%
Neural Network Method
33%
Surrogate Model
33%
Efficient Optimization
33%
Ground Truth Value
33%
Dual Neural Network
33%
OpenKIM
33%
Engineering
Interatomic Potential
100%
Atomistic Simulation
66%
Model Parameter
66%
Desired Property
33%
Parameter Data
33%
Neural Network Approach
33%
Surrogate Model
33%
Parameter Set
33%
Abstract Training
33%
Computer Science
Optimization Algorithm
100%
Density Functional Theory
66%
Parameter Value
33%
Training Data
33%
Neural Network Approach
33%
Desired Property
33%
Process Optimization
33%
Mathematics
Interatomic Potential
100%
Density Functional
66%
Neural Network
33%
Truth Value
33%
Training Data
33%
Material Science
Density
100%
Materials Property
100%
Chemical Engineering
Neural Network
100%