Patrick Mvoto Kongo | Searcharxiv

arXiv2025

From orbital analysis to active learning: an integrated strategy for the accelerated design of TADF emitters

Thermally Activated Delayed Fluorescence (TADF) emitters must satisfy two competing requirements: small singlet-triplet energy gaps for thermal upconversion and sufficient spin-orbit coupling for fast reverse intersystem crossing. Predicting these properties accurately demands expensive calculations. We address this using a validated semi-empirical protocol (GFN2-xTB geometries, sTDA/sTD-DFT-xTB excited states) on 747 molecules, combined with charge-transfer descriptors from Natural Transition Orbital analysis. The hole-electron spatial overlap She emerges as a key predictor, accounting for 21% of feature importance for the triplet state alone. Our best model (Support Vector Regression) reaches MAE = 0.024 eV and R2 = 0.96 for $ΔE_{ST}$. Active learning reduces the data needed to reach target accuracy by approximately 25% compared to random sampling. Three application domains are explored: NIR-emitting probes for bioimaging, photocatalytic sensitizers, and fast-response materials for photodetection.

cond-mat.mtrl-sci↗

arXiv2025

Comparative Analysis of GFN Methods in Geometry Optimization of Small Organic Semiconductor Molecules: A DFT Benchmarking Study

This study benchmarks the GFN family of semiempirical methods (GFN1-xTB, GFN2-xTB, GFN0-xTB, and GFN-FF) against density functional theory (DFT) for the evaluation of optimized molecular geometries and electronic properties of small organic semiconductor molecules. This work offers a systematic assessment of these computationally efficient quantum chemical methods and their accuracy-cost profiles when applied to a challenging class of systems, characterized, for instance, by extended $π$-conjugation, conformational flexibility, and sensitivity of properties to subtle structural changes. Two datasets are evaluated: a QM9-derived subset of small organic molecules and the Harvard Clean Energy Project (CEP) database of extended $π$-systems relevant to organic photovoltaics. Structural agreement is quantified using heavy-atom RMSD, equilibrium rotational constants, bond lengths, and angles, while electronic property prediction is assessed via HOMO-LUMO energy gaps. Computational efficiency is assessed via CPU time and scaling behavior. GFN1-xTB and GFN2-xTB demonstrate the highest structural fidelity, while GFN-FF offers an optimal balance between accuracy and speed, particularly for larger systems. The results indicate that GFN-based methods are suitable for high-throughput molecular screening of small organic semiconductors, with the choice of method depending on accuracy-cost trade-offs. The findings support the deployment of GFN approaches in computational pipelines for the discovery of organic electronics and materials, providing information on their strengths and limitations relative to established DFT methods.

cond-mat.mtrl-sci↗