2023/08/07 by Nicolas Jolly, Jolly, Nicolas, Yuriel Núñez Fernández +3 · 7 citations
Materials Science · Biochemistry, Genetics and Molecular Biology · Chemistry · #Machine Learning in Materials Science #Protein Structure and Dynamics #Advanced NMR Techniques and Applications
paper · pdf · doi:10.48550/arxiv.2308.03508
Choosing a basis set is the first step of a quantum chemistry calculation and it sets its maximum accuracy. This choice of orbitals is limited by strong technical constraints as one must be able to compute a large number of six dimensional Coulomb integrals from these orbitals. Here we use tensor network techniques to construct representations of orbitals that essentially lift these technical constraints. We show that a large class of orbitals can be put into ``tensorized'' form including the Gaussian orbitals, Slater orbitals, linear combination thereof as well as new orbitals beyond the above. Our method provides a path for building more accurate and more compact basis sets beyond what has been accessible with previous technology. As an illustration, we construct optimized tensorized orbitals and obtain a 85% reduction of the error on the energy of the H2 molecules with respect to a reference double zeta calculation (cc-pvDz) of the same size.