The development of semiempirical models to simplify quantum mechanical descriptions of atomistic systems is a practice that started soon after the discovery of quantum mechanics and continues to the present day. There are now many methods for atomistic simulation with many software implementations and many users on a scale large enough to be considered as a software market. Semiempirical models occupied a large share of this market in its early days, but research activity in atomistic simulation has steadily polarized over the last three decades toward general-purpose but expensive ab initio quantum mechanics methods and fast but special-purpose molecular mechanics methods. I offer a perspective on recent trends in atomistic simulation from the middle ground of semiempirical modeling, to learn from its past success and consider its possible paths to future growth. In particular, there is a lot of ongoing research activity in combining semiempirical quantum mechanics with machine learning models and some unrealized possibilities for tighter integration between ab initio and semiempirical quantum mechanics with more flexible theoretical frameworks and more modular software components.
Jonathan E. Moussa (Wed,) studied this question.