Density Functional Theory (DFT) is the most widely used method in computational physics and chemistry. However, the accuracy of existing semi-local functionals is inadequate for many systems, leading to charge delocalization, self-interaction errors and inconsistent treatment of static correlation.

Using machine learning in combination with exact physical constraints and efficient descriptors, we are developing models to approximate the universal exchange-correlation (XC) functional of DFT. Our goal is to obtain a transferable functional capable of computing ground-state properties of systems that are too complex for conventional XC functionals and too large for advanced quantum chemistry methods.

Papers on CIDER functionals: 

• https://pubs.acs.org/doi/abs/10.1021/acs.jctc.1c00904

• https://journals.aps.org/prb/abstract/10.1103/PhysRevB.110.075130

• https://pubs.acs.org/doi/10.1021/acs.jctc.4c00999

CIDER Code: https://github.com/mir-group/CiderPress.