The Enduring Relevance of Semiempirical Quantum Mechanics

TL;DR

Semiempirical quantum mechanics models, developed early in quantum theory, remain relevant today as they balance computational efficiency and accuracy, with ongoing innovations integrating machine learning and hybrid approaches.

Contribution

The paper provides a perspective on the historical significance, current trends, and future potential of semiempirical quantum mechanics in atomistic simulations.

Findings

Semiempirical models historically dominated atomistic simulation software.

Recent research focuses on combining semiempirical methods with machine learning.

Potential for tighter integration between ab initio and semiempirical methods exists.

Abstract

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 the research activity in atomistic simulation has steadily polarized over the last three decades towards general-purpose but expensive ab initio quantum mechanics methods and fast but special-purpose molecular mechanics methods. I offer 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 of tighter integration between ab initio and semiempirical quantum mechanics with more flexible theoretical frameworks and more modular software components.