2026/03/13 by Kyle Acheson, Rastislav Turanyi, Scott Habershon · 1 voice
Materials Science · Physics and Astronomy · #Advanced Chemical Physics Studies #Machine Learning in Materials Science #Spectroscopy and Quantum Chemical Studies
paper · pdf · doi:10.1021/jacs.5c22323
openalex publication_date 2026/03/13 · openalex created_date 2026/03/14 · openalex updated_date 2026/06/15
High Resolution Image Download MS PowerPoint Slide We demonstrate a physics-informed program synthesis (PIPS) approach that can be used to identify entirely new algorithms that approximate the results of single-reference electronic structure approaches like Hartree–Fock (HF) and density-functional theory (DFT)─but without any self-consistent field iterations at all. Our PIPS strategy exploits the fact that the eigenvectors of the Fock matrix F (or Kohn–Sham matrix K ) are the same as the eigenvectors of a broad class of matrix functions, f ( F ). As a result, PIPS can be used to seek matrices M that yield the same molecular orbital coefficients as converged HF or DFT calculations. We demonstrate this approach by generating new algorithms that accurately predict total energies for a series of heterodiatomic molecules (LiCl, LiF, NaCl, NaF) and C 1 –C 4 hydrocarbons; further simulations of C 8 –C 20 alkane species demonstrate further transferability and efficiency of the resulting algorithms. We obtain novel algorithms that can reproduce HF or DFT energies to within 0.1 kcal/mol/atom while requiring only a single matrix-diagonalization operation, rather than an iterative self-consistent field convergence. The approach demonstrated here could be similarly applied to more complex wave function ansatze, opening an interesting optimization-based pathway to identifying accurate yet efficient algorithms for molecular quantum chemistry.