CMD‐FEP: Machine‐Learned Free‐Energy Prediction for Efficient Screening of Material Interfacial Binder

ABSTRACT Identifying interfacial binders for energetic materials remains constrained by the inherent trade‐off between computational speed and predictive fidelity. Experimental screening is labor‐intensive, while empirical force‐field molecular dynamics (MD) offers efficiency at the cost of accuracy. Conversely, high‐fidelity approaches such as free‐energy perturbation (FEP) are computationally prohibitive for large chemical spaces. Here, we develop conventional molecular dynamics‐free energy perturbation(cMD‐FEP), an automated Uni‐Mol‐based machine learning framework that seamlessly bridges conventional MD and FEP to enable high‐fidelity, high‐throughput screening of interfacial binders. cMD‐FEP employs staged, independent task‐specific fine‐tuning—first on large‐scale MD trajectories and subsequently on FEP data—yielding an end‐to‐end predictive pipeline that directly maps molecular structures to interfacial interaction and free energies. Trained on datasets of ∼6.3 × 10 4 simulation‐derived entries, cMD‐FEP achieves FEP‐level accuracy with orders‐of‐magnitude acceleration, screening ∼10 6 octogen (HMX)–binder pairs within 10 min and predicting reliable free energies for ∼3 × 10 4 candidates. Clustering of top‐ranking binders identifies several noncanonical chemotypes with superior adhesion strength. Experimental validation on nitrogen‐containing binders confirms the predicted free‐energy trends, demonstrating strong agreement with cMD‐FEP results. This framework exhibits remarkable generalizability and robustness, offering a potential route for data‐driven discovery of organic interfacial functional materials beyond energetic systems.