Infusing Experimental Reality into Complex Many-Body Hamiltonians: The Observable-Constrained Variational Framework (OCVF)

TL;DR

The paper introduces the Observable-Constrained Variational Framework (OCVF), a method to incorporate experimental data into many-body Hamiltonian models, improving their physical accuracy and predictive power.

Contribution

It develops a theoretically grounded, numerically feasible approach to correct theoretical models using experimental observables, demonstrated on BaTiO3.

Findings

Improved phase transition temperature predictions by 95.8%.

Enhanced lattice structure accuracy in the Rhombohedral phase by 55.6%.

Validated the framework's effectiveness in calibrating models with experimental data.

Abstract

Deep learning potentials for complex many-body systems often face challenges of insufficient accuracy and a lack of physical realism. This paper proposes an "Observable-Constrained Variational Framework" (OCVF), a general top-down correction paradigm designed to infuse physical realism into theoretical "skeleton" models (H_o) by imposing constraints from macroscopic experimental observables (\mathfrak{O}_{\text{exp},s}). We theoretically derive OCVF as a numerically tractable extension of the "Constrained-Ensemble Variational Method" (CEVM), wherein a neural network (\Delta H_\theta) learns the correction functional required to match the experimental data. We apply OCVF to BaTiO3 (BTO) to validate the framework: a neural network potential trained on DFT data serves as H_o, and experimental PDF data at various temperatures are used as constraints (\mathfrak{O}{\text{exp},s}). The final model, H_o + \Delta H_\theta, successfully predicts the complete phase transition sequence accurately (s', s \neq s'). Compared to the prior model, the accuracy of the Cubic-Tetragonal (C-T) phase transition temperature is improved by 95.8\% , and the Orthorhombic-Rhombohedral (O-R) T_c accuracy is improved by 36.1\%. Furthermore, the lattice structure accuracy in the Rhombohedral (R) phase is improved by 55.6\%, validating the efficacy of the OCVF framework in calibrating theoretical models via observational constraints.