Skip to content
Log in
Open access

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

Qi He Pengju Wang Xudong He Jincang Zhang Yi Liu
Aug 2026 · Molecules · Vol 31 · 0 citations · 86 references
Medicine

TL;DR

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.

Abstract

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.

DOI PubMed Semantic Scholar
Read PDF

Similar papers

Preprint Jul 2026

Learning to Converge: Warm-Starting DFTB Self-Consistent Charges with Machine Learning

A machine learning approach is presented that accelerates DFTB simulations by predicting optimal initial atomic charges and demonstrates that ML-predicted initial charges consistently and significantly improve SCC convergence across diverse chemical systems including organic molecules, biomolecules, water clusters, transition metal oxides and solid electrolytes.

Maximilian L. Ach, Karsten Reuter, C. Panosetti · 0 citations
Open access Aug 2026

Modeling dual-range atomic interactions with physicochemical principles for molecular force fields

GeoNet is a physicochemical-principle-guided framework for modeling dual-range atomic interactions that achieves the smallest model size and the shortest training time, demonstrating both superior predictive performance and computational efficiency.

Honghao Wang, Zunlong Liu, Xiangxiang Zeng et al. · 0 citations
Jul 2026

Data-Driven Exploration of the Polyethylene Catalyst Chemical Space via Machine Learning.

A data-driven framework combining explainable machine learning (ML) with large-scale virtual library generation with large-scale virtual library generation is presented, establishing a practical route from experimental data to actionable catalyst designs.

Xuefeng Li, Haoke Qiu, Hanwen Pei et al. · 0 citations
Open access Jul 2026

Incorporating a neural network into the AMOEBA polarizable force field for ligand field effects of Cu2+ ions

Characterizing the solvation of open-shell transition metal ions remains a challenge for classical force fields due to ligand field electronic effects. Here, we present AMOEBA + NN, a machine-learning-augmented polarizable potential, to investigate Cu2+ solvation in NH3 and H2O and mixed environments. The model, trained on quantum-mechanical (QM) association energies, accurately reproduces the Cu2+ energy landscape across diverse coordination geometries. Molecular dynamics simulations demonstrate that AMOEBA + NN successfully captures the electronically driven Jahn–Teller (JT) distortion. Evidence from radial distribution functions (RDFs) and geometry optimizations reveals characteristic axial elongation, which is absent in conventional classical descriptions. Kinetic analysis shows a highly dynamic Cu2+–NH3 environment with a rapid ligand exchange (residence time of 66.52 ps), contrasting with the H2O system where binding is two to three orders of magnitude more stable. Furthermore, the calculated hydration free energy of −477.59 ± 0.50 kcal mol−1 shows excellent agreement with experimental data (within 1.31 kcal mol−1). This work provides a unified, computationally efficient framework for describing the structural, dynamic, and thermodynamic observables of transition metal coordination.

Zhecheng He, Yanxing Wang, Shubham Chatterjee et al. · 0 citations
Open access Aug 2026

Transferable Collective Variable to accelerate Protein-Ligand (Un)Binding Transitions via Explainable Machine Learning and Intriguing Role of Ligand Solvation

This study presents a method to derive optimized CV from transition state region (TS) via an interpretable machine learning (ML) model, Elastic Net, which greatly accelerate ligand binding-unbinding transitions and achieves rapid free energy surface (FES) convergence across diverse systems.

Saikat Dhibar, B. Jana · 0 citations
Preprint Jul 2026

Correcting DFT formation energies towards experimental accuracy using foundational MLIPs and latent-feature delta-learning

This work demonstrates how recent foundational machine learning interatomic potentials (MLIPs) trained at the r$^2$SCAN level can be leveraged to improve the agreement of formation energies with experiment, reducing the mean absolute error by more than 40% relative to GGA without requiring any additional DFT calculation.

Timo Reents, Marnik Bercx, Giovanni Pizzi · 0 citations