Related Experiment Video
Updated: Dec 10, 2025

Rapid Scan Electron Paramagnetic Resonance Opens New Avenues for Imaging Physiologically Important Parameters In Vivo
Published on: September 26, 2016
Accurate and Numerically Efficient r2SCAN Meta-Generalized Gradient Approximation
James W Furness1, Aaron D Kaplan2, Jinliang Ning1
1Department of Physics and Engineering Physics, Tulane University, New Orleans, Louisiana 70118, United States.
Abstract:
The recently proposed rSCAN functional [ J. Chem. Phys. 2019 150, 161101] is a regularized form of the SCAN functional [ Phys. Rev. Lett. 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.
Related Concept Videos
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
Gradient and Del Operator
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation
On...
Improving Translational Accuracy
Improving Translational Accuracy
Calibration Curves: Linear Least Squares
For data that follow a straight line, the standard method for fitting is the linear...

