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Single-Parameter Scaling Strategy for Force Field Optimization: A Case Study on Alkane Melting-Point Prediction

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Figshare2025-10-10 更新2026-04-28 收录
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https://figshare.com/articles/dataset/Single-Parameter_Scaling_Strategy_for_Force_Field_Optimization_A_Case_Study_on_Alkane_Melting-Point_Prediction/30332399
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Classical force fields critically determine the reliability of molecular simulations, yet simultaneous optimization of their high-dimensional parameters remains inefficient and risks disrupting cross-system compatibility. Using alkane melting points as the target property, this work systematically investigates how scaling individual parameters in multiscale force fields affects prediction accuracy. Three linear alkanes (octane (C8), hexadecane (C16), and tetracosane (C24)) were evaluated with two all-atom (AA) models (L-OPLS, CHARMM36), three united-atom (UA) models (TraPPE-UA, PYS, and OPLS-UA), and one coarse-grained (CG) model (Martini 3). Unmodified force field tests showed that L-OPLS, CHARMM36, PYS, and TraPPE-UA predicted melting points within 7% of experimental values, whereas OPLS-UA consistently overpredicted all alkane melting points, and Martini 3 exhibited significant overprediction for C8. Parameter scaling revealed that, for the UA models, bond force constant (kb) and angle force constant (ka) negligibly impact melting points, whereas melting points positively correlate with dihedral force constant (kn) and Lennard-Jones (LJ) parameters (ε, σ). Since LJ parameter scaling substantially perturbs liquid densities and self-diffusion coefficientsunlike dihedral adjustmentsscaling kn emerges as the optimal strategy. Melting points were corrected to experimental values with ± 10% kn scaling for TraPPE-UA and PYS, whereas OPLS-UA required ≈50% reduction of kn. For AA models, partial charge scaling effectively tuned melting points with minimal effects on the liquid properties. In Martini 3, angle force constant scaling improved predictions (except for angle-lacking C8). This systematic single-parameter scaling (SPS) delineates structure–property relationships between force field parameters and alkane thermophysical behavior, providing an efficient strategy for the rapid refinement of established force fields.
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