“Gold-Standard” Δ-Machine Learned Transferable Potential for Linear Alkanes
The conformational properties of linear alkanes, C<sub><i>n</i></sub>H<sub>2<i>n</i>+2</sub>, have been of intense interest for many years. Experiments and corresponding electronic structure calculations were first reported in the mid-2000s and continue to the present time. These focus on the minimum chain length where the transition from the linear minimum to the hairpin minimum occurs. We recently reported a transferable, many-body permutationally invariant polynomial (MB-PIP) potential for linear alkanes trained on roughly 253 000 B3LYP electronic energies for C<sub>14</sub>H<sub>30</sub>. Here we report a Δ-ML approach to elevate this B3LYP-based and a new PBE0+MBD MB-PIP potential using roughly 4500 Pair Natural Orbital Local Coupled Cluster (PNO-LCCSD(T)-F12) energies. The new Δ-corrected potentials predict the difference in these minima accurately, compared to benchmark PNO-LCCSD(T)-F12 energies results, over the range C<sub>12</sub>H<sub>28</sub> to C<sub>28</sub>H<sub>58</sub>. Vibrational power spectra are also reported for C<sub>14</sub>H<sub>30</sub> using the original B3LYP-based and Δ-ML corrected potentials. These new MB-PIP potentials for linear alkanes are the most accurate ones currently available and can be used in studies of properties of linear alkanes.
Authors
- Qi Yu (ORCID: https://orcid.org/0000-0002-2030-0671)
- Apurba Nandi (ORCID: https://orcid.org/0000-0002-6191-5584)
- Paul L. Houston (ORCID: https://orcid.org/0000-0003-2566-9539)
- Riccardo Conte (ORCID: https://orcid.org/0000-0003-3026-3875)
- Joel M. Bowman (ORCID: https://orcid.org/0000-0001-9692-2672)
- Chen Qu (ORCID: https://orcid.org/0000-0001-8889-4851)
Institutions
- Emory University (US)
- University of Luxembourg (LU)
- University of Milan (IT)
- Cornell University (US)
- Fudan University (CN)
- Emerson (United States) (US)
- Atlanta University Center (US)
Publication Details
- Journal
- The Journal of Physical Chemistry Letters
- Published
- 2025-11-20
- DOI
- https://doi.org/10.1021/acs.jpclett.5c02946
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 0.00