Wave Functions, Density Functionals, and Artificial Intelligence for Materials and Energy Research: Future Prospects and Challenges

Semiconducting materials, crystalline or amorphous, feature a diverse family of emergent transient properties (excitons, free carriers, plasmons, polarons, etc.) of interest to energy science, which are observed (indirectly or directly) in carefully designed experiments. Theoretical methods, which provide detailed and accurate information about the excitations of small molecules, have trouble with large systems because of computational limitations, such that a thorough selection of algorithms plays a crucial role. With a wide range of research opportunities in mind, in this Perspective we consider, from a first-principles perspective, the techniques available to calculate optical and electronic properties of materials and discuss (i) challenges in density-functional and wave function methods for materials and energy science, (ii) a method developed by us for describing excited-state phenomena (which consists of the linear response analysis of perturbed initial states), and (iii) opportunities for using machine learning in computational and theoretical chemistry studies