2026-10-14T07:24:53Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33891643 2026-09-17T12:40:30Z portal_1197 item_type_3 month_year_09_2026

Data for Exploring new algorithms for molecular vibrational spectroscopy using physics-informed program synthesis Kyle Acheson (15388816) Scott Habershon (1263891) Programming by example (Computer science) Algorithms Quantum chemistry Hamiltonian systems Mathematical optimization Migrated from ePrints Inductive program synthesis (PS) has recently begun to emerge as a useful new approach to automatically generating algorithms in quantum chemistry, as demonstrated in recent applications to the vibrational Schrodinger equation for simple model systems with one or two degrees-of-freedom. Here, we report a new physics-informed approach to inductive PS that is more conducive to generating discrete variable representation (DVR) algorithms for real molecular systems. The new framework ensures separability of the kinetic and potential operators and does not require an exact solution to compare synthesised algorithmic predictions with. Algorithms with a tri-diagonal matrix structure are generated via a variational based stochastic optimisation procedure. Crucially, through an extensive testing procedure we demonstrate that variationally synthesised algorithms perform just as well as those generated using a target function. Assuming a direct product representation of normal coordinates, these algorithms are applied to three tri-atomic molecules. In total, we identify a set of seven PS algorithms that accurately reproduce the vibrational spectra of H2O, NO2, and SO2, as predicted by Colbert-Miller and sine-DVR algorithms. 2025-07-03T00:00:00Z Dataset Dataset 10.82444/warw.33891643.v1 https://figshare.com/articles/dataset/Data_for_Exploring_new_algorithms_for_molecular_vibrational_spectroscopy_using_physics-informed_program_synthesis/33891643 CC BY 4.0