Targeted Transferable Machine-Learned Potential for Linear Alkanes Trained on C₁₄H₃₀ and Tested for C₄H₁₀ to C₃₀H₆₂

Given the great importance of linear alkanes in fundamental and applied research, an accurate machine-learned potential (MLP) would be a major advance in computational modeling of these hydrocarbons. Recently, we reported a novel, many-body permutationally invariant model that was trained specifically for the 44-atom hydrocarbon C₁₄H₃₀ on roughly 250,000 B3LYP energies (Qu, C.; Houston, P. L.; Allison, T.; Schneider, B. I.; Bowman, J. M. <i>J. Chem. Theory Comput.</i> <b>2024</b>, <i>20</i>, 9339-9353). Here, we demonstrate the accuracy of the transferability of this potential for linear alkanes ranging from butane C₄H₁₀ up to C₃₀H₆₂. Unlike other approaches for transferability that aim for universal applicability, the present approach is targeted for linear alkanes. The mean absolute error (MAE) for energy ranges from 0.26 kcal/mol for butane and rises to 0.73 kcal/mol for C₃₀H₆₂ over the energy range up to 80 kcal/mol for butane and 600 kcal/mol for C₃₀H₆₂. These values are unprecedented for transferable potentials and indicate the high performance of a targeted transferable potential. The conformational barriers are shown to be in excellent agreement with high-level ab initio calculations for pentane, the largest alkane for which such calculations have been reported. Vibrational power spectra of C₃₀H₆₂ from molecular dynamics calculations are presented and briefly discussed. Finally, the evaluation time for the potential is shown to vary linearly with the number of atoms.