Constructing Accurate and Efficient General-Purpose Atomistic Machine Learning Model with Transferable Accuracy for Quantum Chemistry

Density
functional theory (DFT) has been a cornerstone
in computational
science, providing powerful insights into structure–property
relationships for molecules and materials through first-principles
quantum-mechanical (QM) calculations. However, the advent of atomistic
machine learning (ML) is reshaping the landscape by enabling large-scale
dynamics simulations and high-throughput screening at DFT-equivalent
accuracy with drastically reduced computational cost. Yet, the development
of general-purpose atomistic ML models as surrogates for QM calculations
faces several challenges, particularly in terms of model capacity,
data efficiency, and transferability across chemically diverse systems.
This work introduces a novel extension of the polarizable atom interaction
neural network (namely, XPaiNN) to address these challenges. Two distinct
training strategies have been employed, one direct-learning and the
other Δ-ML on top of a semiempirical QM method. These methodologies
have been implemented within the same framework, allowing for a detailed
comparison of their results. The XPaiNN models, in particular the
one using Δ-ML, not only demonstrate competitive performance
on standard benchmarks, but also demonstrate the effectiveness against
other ML models and QM methods on comprehensive downstream tasks,
including noncovalent interactions, reaction energetics, barrier heights,
geometry optimization and reaction thermodynamics, etc. This work
represents a significant step forward in the pursuit of accurate and
efficient atomistic ML models of general-purpose, capable of handling
complex chemical systems with transferable accuracy.