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

ABSTRACT: Hydrocarbons are ubiquitous as fuels, solvents, lubricants, and as the principal components of plastics and fibers, yet our ability to predict their dynamical properties is limited to force-field 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 density functional theory (DFT) (B3LYP) energies for a large variety of configurations, obtained using MM3 direct-dynamics calculations at 500, 1000, 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 dir

SUBMITTER: Qu C 

PROVIDER: S-EPMC11562071 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature