Lessons Learned from Catalysis to Qubits: General Strategies to Build Accessible and Accurate First-Principles Models...

Defects and dopants play critical roles in defining the properties of a material. Achieving a mechanistic understanding of how such properties arise is challenging with current experimental methods, and computational approaches suffer from significant modeling limitations that frequently require a posteriori fitting. Consequently, the pace of dopant discovery as a means of tuning material properties for a particular application has been slow. However, recent advances in computation have enabled researchers to move away from semiempirical schemes to reposition density functional theory as a predictive tool and improve the accessibility of highly accurate first-principles methods to all researchers. This Perspective discusses some of these recent achievements that provide more accurate first-principles geometric, thermodynamic, optical, and electronic properties simultaneously. Advancements related to supercells, basis sets, functionals, and optimization protocols, as well as suggestions for evaluating the quality of a computational model through comparison to experimental data, are discussed. Moreover, recent computational results in the fields of energy materials, heterogeneous catalysis, and quantum informatics are reviewed along with an evaluation of current frontiers and opportunities in the field of computational materials chemistry.