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Abstract The theorems of density functional theory (DFT) establish bijective maps between the local external potential of a many-body system and its electron density, wavefunction and, therefore, one-particle reduced density matrix. Building on this foundation, we show that machine learning models based on the one-electron reduced density matrix can be used to generate surrogate electronic structure methods. We generate surrogates of local and hybrid DFT, Hartree-Fock and full configuration interaction theories for systems ranging from small molecules such as water to more complex compounds like benzene and propanol. The surrogate models use the one-electron reduced density matrix as the central quantity to be learned. From the predicted density matrices, we show that either standard quantum chemistry or a second machine-learning model can be used to compute molecular observables, energies, and atomic forces. The surrogate models can generate essentially anything that a standard electronic structure method can, ranging from band gaps and Kohn-Sham orbitals to energy-conserving ab-initio molecular dynamics simulations and infrared spectra, which account for anharmonicity and thermal effects, without the need to employ computationally expensive algorithms such as self-consistent field theory. The algorithms are packaged in an efficient and easy to use Python code, QMLearn, accessible on popular platforms.
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This content will become publicly available on August 14, 2026
A Restriction-based Configuration Interaction Approach based on LC-DFTB: A Scalable Method for Field-Induced Charge Transfer in Molecular Systems
Investigating electron transfer behavior under external electric fields in molecular electronics is crucial for understanding the function of each component and for improving molecular design. Notably, the one-electron transfer is inevitable in molecular wires and switches, for which traditional density functional theory (DFT) and long-range corrected self-consistent-charge density functional tight binding (LC-DFTB) meet significant challenges. Inspired by previous studies on constrained configuration interaction schemes, we present restriction-based configuration interaction (RCI) LC-DFTB, a novel extension of LC-DFTB to deliver an accurate description of one-electron transfer under external electric fields. This approach retains the low cost of LC-DFTB while accurately capturing charge-resonance, localization versus delocalization, and field-induced response in large, structurally complex systems. We demonstrate its performance on a benzene assembly and a polyfluorene, showing that RCI-LC-DFTB efficiently describes the effects of molecular conformation and applied bias on electron localization and transfer. Our method thus provides a robust, scalable tool for the design of molecular electronic and organic photovoltaic materials.
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- Award ID(s):
- 1848067
- PAR ID:
- 10644630
- Publisher / Repository:
- ChemRxiv
- Date Published:
- Edition / Version:
- 1
- Subject(s) / Keyword(s):
- LC-DFTB configuration interaction constrained DFTB field-induced charge transfer
- Format(s):
- Medium: X Size: 1.2 MB Other: pdf
- Size(s):
- 1.2 MB
- Sponsoring Org:
- National Science Foundation
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