What's the gap? A possible strategy for advancing theory, and an appeal for experimental structure data to drive that...

There is substantial demand for theoretical/computational tools that can produce correct predictions of the geometric structure and band gap to accelerate the design and screening of new materials with desirable electronic properties. DFT-based methods exist that reliably predict electronic structure given the correct geometry. Similarly, when good spectroscopic data are available, these same methods may, in principle, be used as input to the inverse problem of generating a good structural model. The same is generally true for gas-phase systems, for which the choice of method is different, but factors that guide its selection are known. Despite these successes, there are shortcomings associated with DFT for the prediction of materials' electronic structure. The present paper offers a perspective on these shortcomings. Fundamentally, the shortcomings associated with DFT stem from a lack of knowledge of the exact functional form of the exchange-correlation functional. Inaccuracies therefore arise from using an approximate functional. These inaccuracies can be reduced by judicious selection of the approximate functional. Other apparent shortcomings present due to misuse or improper application of the method. One of the most significant difficulties is the lack of a robust method for predicting electronic and geometric structure when only qualitative (connectivity) information is available about the system/material. Herein, some actual shortcomings of DFT are distinguished from merely common improper applications of the method. The role of the exchange functional in the predicted relationship between geometric structure and band gap is then explored, using fullerene, 2D polymorphs of elemental phosphorus and polyacetylene as case studies. The results suggest a potentially fruitful avenue of investigation by which some of the true shortcomings might be overcome, and serve as the basis for an appeal for high-accuracy experimental structure data to drive advances in theory.