TDDFT
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Related Articles from SNS
A Unified Heterogeneous Implementation of Numerical Atomic Orbitals-Based Real-Time TDDFT within the ABACUS Package
Announce Type: replace-cross Abstract: We present a unified heterogeneous computing framework for real-time time-dependent density functional theory (RT-TDDFT) based on numerical atomic orbitals (NAOs), implemented in the ABACUS package. We introduce three co-designed abstraction layers, including unified data containers, unified linear algebra operators, and unified grid integration interfaces. These layers collectively accelerate the two most demanding parts of NAO-based RT-TDDFT: explicit...
RPA as a Hessian Closure: Effective Functionals and Source-Variable Duality Across DFT, LR-TDDFT, 1RDMFT, and MBPT
Announce Type: new Abstract: We present a variational formulation of the random phase approximation (RPA) that places density functional theory (DFT), linear-response time-dependent density functional theory (LR-TDDFT), one-body reduced density matrix functional theory (1RDMFT), and Green's function many-body perturbation theory (MBPT) into a common source-variable hierarchy. The central claim is that RPA is not best defined by any one problem-specific formula, diagrammatic resummation, or...
Analytic first-order non-adiabatic coupling matrix elements of spin-adapted open-shell time-dependent density functional theory
arXiv:2605.26594v2 Announce Type: replace Abstract: While spin-adapted time-dependent density functional theory (TDDFT) approaches significantly improve the excitation energies and gradients of open-shell molecules, the effect of spin-adaptation on non-adiabatic coupling matrix elements (NACMEs) remains unknown for spin-conserving excitations. In this article, we report the derivation, implementation and benchmark studies of the ground state-excited state and excited state-excited state...
Excited States from Restricted Open Shell Plane-Wave DFT
Announce Type: replace Abstract: Variational excited-state density functional theory (DFT) enables the calculation of excited states at a cost comparable to ground-state calculations, but single-configuration approaches often suffer from spin contamination. We implement restricted open-shell Kohn-Sham (ROKS) DFT, which recovers spin-pure singlet excitation energies via the variational minimization of a weighted combination of mixed-spin and triplet configurations, within the plane-wave...
Derivative Informed Learning of Exchange-Correlation Functionals
arXiv:2606.04279v1 Announce Type: new Abstract: Machine-learned (ML) exchange-correlation (XC) functionals aim to replace human-designed density functional approximations by learning directly from reference data, but they still do not consistently outperform traditional $\mathcal{O}(N^4)$-scaling hybrid functionals. We study a hybrid-distillation setting in which $\mathcal{O}(N^3)$-scaling ML-XC functionals are trained to reproduce B3LYP/def2-SVP targets. We introduce Derivative Informed...
De novo molecular generation with optical property preconditioning at the token level
Announce Type: new Abstract: Designing OLED molecules with targeted optical properties remains challenging due to the scarcity of high-quality data and the limited reliability of conditional control in generative models across chemical motifs. Here, we benchmark a token-conditioned autoregressive language model for OLED molecular generation in a realistic low-data regime. A GPT2 model is pretrained on large chemical corpora, augmented with discrete property tokens, and fine-tuned using...