Accurate and Numerically Efficient r 2 SCAN Meta-Generalized Gradient Approximation
Tulane University · Temple University
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Abstract
2015 115, 036402] that improves SCAN's numerical performance at the expense of breaking constraints known from the exact exchange-correlation functional. We construct a new meta-generalized gradient approximation by restoring exact constraint adherence to rSCAN. The resulting functional maintains rSCAN's numerical performance while restoring the transferable accuracy of SCAN.
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- Constraint (computer-aided design)
- Mathematics
- Applied mathematics
- Construct (python library)
- Physics
- Mathematical analysis
- Computer science
- Geometry
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