Improving the performance of fermionic neural networks with the Slater exponential Ansatz

Abstract

AbstractIn this work, we propose a technique for the use of fermionic neural networks (FermiNets) with the Slater exponential Ansatz for electron‐nuclear and electron–electron distances, which provides faster convergence of target ground‐state energies due to a better description of the interparticle interaction in the vicinities of the coalescence points, while the accuracies of results, obtained with original and input‐modified FermiNets are similar due to high expressiveness of underlying neural network. Our analysis of learning curves indicates on the possibility to obtain accurate energies with smaller batch sizes using arguments of the bagging approach. In order to obtain even more accurate results for the ground‐state energies, we propose an extrapolation scheme for estimating Monte Carlo integrals in the limit of an infinite number of points. Numerical tests for a set of molecules demonstrate a good agreement with the results of the original FermiNets approach (achieved with larger batch sizes than required by our approach) and with results of the coupled‐cluster singles and doubles with perturbative triples (CCSD(T)) method that are calculated in the complete basis set limit.