Machine Learning-Driven Refinement of Reactive Force Fields via Hierarchical "Center-Environment" Features for Energetic Molecular Crystals.

Accurate energy prediction in organic molecular crystals, such as cyclotrimethylenetrinitramine (RDX) and cyclotetramethylenetetranitramine (HMX), is hindered by the scarcity of descriptors capable of encoding their hierarchical architecture. Furthermore, while reactive force field (ReaxFF) offers a pathway to simulating chemical reactions, its accuracy for polymorph stability remains suboptimal, and machine learning (ML) driven refinement of ReaxFF for molecular systems remains unexplored. Herein, we report the first construction of a Hierarchical "Center-Environment" (HCE) feature framework specifically tailored for molecular crystals. The HCE framework hierarchically decomposes structural complexity into intramolecular (ring vs. nitro groups) and intermolecular (central molecule vs. coordination shell) attributes, integrating physics-based priors with distance-weighted attention. Using this compact descriptor set, we present the first attempt to rectify ReaxFF predictions via ML, benchmarking against 5930 RDX and 3335 HMX DFT-calculated energies. Our models achieve a significant reduction in mean absolute error: from ~116.7 to 42.2 meV/atom for RDX (kernel ridge regression, KRR) and from 112.3 to 53.7 meV/atom for HMX (support vector regression, SVR). Crucially, HCE features enable robust cross-molecule transferability; a neural network pre-trained on RDX yields a validation error of 52.9 meV/atom on HMX, surpassing models trained exclusively on HMX data. This work not only establishes a pioneering paradigm for interpretable ML-driven force field refinement but also provides the first feature engineering solution incorporating chemical, physical, and structural information specifically designed for the machine learning of energetic molecular crystals.