Reconstructing Kernel-Based Machine Learning Force Fields with Superlinear Convergence.

ABSTRACT: Kernel machines have sustained continuous progress in the field of quantum chemistry. In particular, they have proven to be successful in the low-data regime of force field reconstruction. This is because many equivariances and invariances due to physical symmetries can be incorporated into the kernel function to compensate for much larger data sets. So far, the scalability of kernel machines has however been hindered by its quadratic memory and cubical runtime complexity in the number of training points. While it is known that iterative Krylov subspace solvers can overcome these burdens, their convergence crucially relies on effective preconditioners, which are elusive in practice. Effective preconditioners need to partially presolve the learning problem in a computationally cheap and num

SUBMITTER: Blucher S 

PROVIDER: S-EPMC10373489 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature