Beyond MD17: the reactive xxMD dataset

TL;DR

This paper introduces the xxMD dataset, a new benchmark for neural force fields involving diverse geometries from non-adiabatic dynamics, addressing the limitations of MD17 in representing chemical reactions.

Contribution

The xxMD dataset extends existing benchmarks by including geometries from non-adiabatic dynamics and multiple electronic structure methods, highlighting challenges in model generalization.

Findings

NFF models show higher errors on xxMD than MD17

Diverse geometries in xxMD better represent chemical reactions

Challenges in creating generalizable NFF models are emphasized

Abstract

System specific neural force fields (NFFs) have gained popularity in computational chemistry. One of the most popular datasets as a bencharmk to develop NFFs models is the MD17 dataset and its subsequent extension. These datasets comprise geometries from the equilibrium region of the ground electronic state potential energy surface, sampled from direct adiabatic dynamics. However, many chemical reactions involve significant molecular geometrical deformations, for example, bond breaking. Therefore, MD17 is inadequate to represent a chemical reaction. To address this limitation in MD17, we introduce a new dataset, called Extended Excited-state Molecular Dynamics (xxMD) dataset. The xxMD dataset involves geometries sampled from direct non-adiabatic dynamics, and the energies are computed at both multireference wavefunction theory and density functional theory. We show that the xxMD dataset involves diverse geometries which represent chemical reactions. Assessment of NFF models on xxMD dataset reveals significantly higher predictive errors than those reported for MD17 and its variants. This work underscores the challenges faced in crafting a generalizable NFF model with extrapolation capability.