Jessica A. Martinez B.

arXiv2026

Machine learning the two-electron reduced density matrix in molecules and condensed phases

Machine learning is rapidly accelerating materials and chemical discovery, but most current models target energies, forces, or selected molecular properties rather than the underlying many-body electronic structure. Learning electronic-structure proxies, such as reduced density matrices, offers a path to surrogates that can predict a broad range of observables from a single ML model. Short of learning the full wavefunction, the two-electron reduced density matrix (2-RDM) is among the most information-rich, minimally lossy targets, providing direct access to expectation values of arbitrary one- and two-electron operators regardless of the strength of the underlying electron correlation. Here we show that learning the 2-RDM is a feasible goal, yielding exceptionally accurate models. We develop surrogates for correlated wavefunction methods (including configuration interaction and coupled cluster) that yield 2-RDMs with sufficient fidelity to provide direct, training-free access to energies and forces for driving energy-conserving molecular dynamics. To tackle realistic molecular condensed phases, we leverage a many-body expansion of the 2-RDM, using our ML models to supply the expansion terms and enabling ML-powered, coupled-cluster-quality electronic structure and energetics for large solvated systems. As a demonstration, we showcase a coupled-cluster-level electronic-structure calculation of glucose solvated by 500 water molecules achieved at Hartree-Fock cost. This work establishes a general framework for learning correlated electronic structure with high fidelity and deploying it to systems beyond the reach of conventional ab initio methods.

physics.chem-ph↗

arXiv2024

Solvation effects on halides core spectra with Multilevel Real-Time quantum embedding

In this work we introduce a novel subsystem-based electronic structure embedding method that combines the projection-based block-orthogonalized Manby-Miller embedding (BOMME) with the density-based Frozen Density Embedding (FDE) methods. Our approach is effective for systems in which the building blocks interact at varying strengths while still maintaining a lower computational cost compared to a quantum simulation of the entire system. To evaluate the performance of our method, we assess its ability to reproduce the X-ray absorption spectra (XAS) of chloride and fluoride anions in aqueous solutions (based on a 50-water droplet model) via real-time time-dependent density functional theory (rt-TDDFT) calculations. We employ an ensemble approach to compute XAS for the K- and L-edges, utilizing multiple snapshots of configuration space obtained from classical molecular dynamics simulations with a polarizable force field. Configurational averaging influences both the broadening of spectral features and their intensities, with contributions to the final intensities originating from different geometry configurations. We found that embedding models that are too approximate for halide-water specific interactions, as in the case of FDE, fail to reproduce the experimental spectrum for chloride. Meanwhile, BOMME tends to overestimate intensities, particularly for higher energy features because of finite-size effects. Combining FDE for the second solvation shell and retaining BOMME for the first solvation shell mitigates this effect, resulting in an overall improved agreement within the energy range of the experimental spectrum. Additionally, we compute the transition densities of the relevant transitions, confirming that these transitions occur within the halide systems. Thus, our real-time QM/QM/QM embedding method proves to be a promising approach for modeling XAS of solvated systems.

physics.chem-ph↗