The Enduring Relevance of Semiempirical Quantum Mechanics

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.