SPA^HM(a,b): Encoding the Density Information from Guess Hamiltonian in Quantum Machine Learning Representations.

ABSTRACT: Recently, we introduced a class of molecular representations for kernel-based regression methods─the spectrum of approximated Hamiltonian matrices (SPA^HM)─that takes advantage of lightweight one-electron Hamiltonians traditionally used as a self-consistent field initial guess. The original SPA^HM variant is built from occupied-orbital energies (i.e., eigenvalues) and naturally contains all of the information about nuclear charges, atomic positions, and symmetry requirements. Its advantages were demonstrated on data sets featuring a wide variation of charge and spin, for which traditional structure-based representations commonly fail. SPA^HM(a,b), as introduced here, expand the eigenvalue SPA^HM into local and transferable representations. They rely

SUBMITTER: Briling KR 

PROVIDER: S-EPMC10867806 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature