DFT-Based Permutationally Invariant Polynomial Potentials Capture the Twists and Turns of C14H30

Hydrocarbons are ubiquitous as fuels, solvents, lubricants, and as the principal components of plastics and fi bers, yet our ability to predict their dynamical properties is limited to force- eld mechanics. Here, we report two machine-learned potential energy surfaces (PESs) for the linear 44-atom hydrocarbon C14H30, using an extensive data set of roughly 250,000 DFT (B3LYP) energies for a large variety of con figurations, obtained using MM3 direct-dynamics calculations at 500 K, 1000 K and 2500 K. The surfaces, based on Permutationally Invariant Polynomials (PIPs) and using both a many-body expansion approach and a fragmented-basis approach, produce precise fits for energies and forces and also produce excellent out-of-sample agreement with direct DFT calculations for torsional and dihedral angle potentials. Going beyond precision, the PESs are used in molecular dynamics calculations that demonstrate the robustness of the PESs for a large range of conformations. The many-body PIPs PES, although more compute intensive than the fragmented-basis one, is directly transferable for other linear hydrocarbons.