Combining state-of-the-art quantum chemistry and machine learning make gold standard potential energy surfaces accessible for medium-sized molecules

12 December 2023, Version 1
This content is a preprint and has not undergone peer review at the time of posting.

Abstract

Developing full-dimensional machine-learned potentials with the current gold-standard coupled-cluster (CC) level is a challenging already for medium-sized molecules due to the high computational cost. Consequently, researchers are often bound to use lower-level electronic structure methods such as density functional theory or second-order Moller-Plesset perturbation theory (MP2). Here, we demonstrate on a representative example that gold-standard potentials can now be effectively constructed for molecules of 15 atoms using off-the-shelf hardware. This is achieved by accelerating the CCSD(T) computations via the accurate and cost-effective frozen natural orbital (FNO) approach. The Delta-machine learning (Delta-ML) approach is employed with the use of permutationally invariant polynomials to fit a full-dimensional PES of the acetylacetone molecule, but any other effective descriptor and ML approach can similarly benefit from the accelerated data generation proposed here. Our benchmarks for the global minima, H-transfer TS, and many high-lying configurations show the excellent agreement of FNO-CCSD(T) results with conventional CCSD(T) while achieving a significant time advantage of about a factor of 30-40. The obtained Delta-ML PES shows high fidelity from multiple perspectives including energetic, structural, and vibrational properties. We obtain the symmetric double well H-transfer barrier of 3.15 kcal/mol in excellent agreement with the direct FNO-CCSD(T) barrier of 3.11 kcal/mol as well as with the benchmark CCSD(F12*)(T+)/CBS value of 3.21 kcal/mol. Furthermore, the tunneling splitting due to H-atom transfer is calculated using a 1D double-well potential, providing improved estimates over previous ones obtained using an MP2-based PES. The methodology introduced here represents a significant advancement in the efficient and precise construction of potentials at the CCSD(T) level for molecules above the current limit of 15 atoms.

Keywords

delta machine learning
potential energy surface
quantum dynamics
tunneling splitting
gold standard quantum chemistry
reduced-cost CCSD(T)
natural orbital approach

Supplementary materials

Title
Description
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Training data
Description
FNO-CCSD(T) and MP2 energies for all conformers used for the training of the delta-ML potential.
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