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Machine learning configuration interaction for ab initio potential energy curves
Jeremy P. Coe
Institute of Chemical Sciences
School of Engineering & Physical Sciences
Research output
:
Contribution to journal
›
Article
›
peer-review
34
Citations (Scopus)
53
Downloads (Pure)
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INIS
curves
100%
potential energy
100%
configuration interaction
100%
machine learning
100%
configuration
60%
neural networks
40%
flies
40%
data
20%
values
20%
comparative evaluations
20%
water
20%
molecules
20%
space
20%
geometry
20%
barriers
20%
errors
20%
spin
20%
nitrogen
20%
carbon monoxide
20%
Chemistry
Configuration Interaction
100%
Potential Energy
100%
Spin State
20%
Purity
20%
Carbon Monoxide
20%
Engineering
Potential Energy
100%
Artificial Neural Network
50%
Considered System
25%
State Function
25%
Pure Spin
25%
Nitrogen Molecule
25%
Chemical Engineering
Learning System
100%
Neural Network
50%
Scalability
25%
Material Science
Carbon Monoxide
100%