How Well Does Kohn-Sham Regularizer Work for Weakly Correlated Systems?

Kohn-Sham regularizer (KSR) is a differentiable machine learning approach to finding the exchange-correlation functional in Kohn-Sham density functional theory that works for strongly correlated systems. Here we test KSR for a weak correlation. We propose spin-adapted KSR (sKSR) with trainable local, semilocal, and nonlocal approximations found by minimizing density and total energy loss.

Learning to Approximate Density Functionals.

Density functional theory (DFT) calculations are used in over 40,000 scientific papers each year, in chemistry, materials science, and far beyond. DFT is extremely useful because it is computationally much less expensive than electronic structure methods and allows systems of considerably larger size to be treated. However, the accuracy of any Kohn-Sham DFT calculation is limited by the approximation chosen for the exchange-correlation (XC) energy.

Protective Effect of Bioactivity Guided Fractions of Ziziphus jujuba Mill. Root Bark against Hepatic Injury and Chronic Inflammation via Inhibiting Inflammatory Markers and Oxidative Stress.

The tribal communities of North Eastern India rely on herbal medicine to cure various disease conditions. Ziziphus jujuba Mill. (Rhamnaceae) is one of such medicinal plants used for curing liver ailments, insomnia, anemia, diarrhea, diabetic complications, cancer, and loss of appetite.