The Enduring Relevance of Semiempirical Quantum Mechanics

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.

16 pages, 4 figures

References in corpus (15)

• DeePMD-kit v2: A software package for Deep Potential models
• Simulating chemistry using quantum computers
• FAIR data enabling new horizons for materials research
• TorchMD: A deep learning framework for molecular simulations
• JARVIS-Leaderboard: A Large Scale Benchmark of Materials Design Methods
• The CECAM Electronic Structure Library and the modular software development paradigm
• A call to arms: making the case for more reusable libraries
• Towards Pair Atomic Density Fitting for Correlation Energies with Benchmark Accuracy
• Enhancing the Accuracy of Density Functional Tight Binding Models Through ChIMES Many-body Interaction Potentials
• Fast and Accurate Prediction of Material Properties with Three-Body Tight-Binding Model for the Periodic Table
• Ultra-Fast Semi-Empirical Quantum Chemistry for High-Throughput Computational Campaigns with Sparrow
• Trends in atomistic simulation software usage
• Transferable empirical pseudopotenials from machine learning
• OpenOrbitalOptimizer -- a reusable open source library for self-consistent field calculations
• Model selection in atomistic simulation