arXiv subjects

Steve Cabrel Teguia Kouam

Publications and source records attributed to Steve Cabrel Teguia Kouam.

5 recordsLinked to original sources

arXiv2026

Temperature-Driven Sequential Modeling for the Prediction of Annual Power Conversion Efficiency Profiles of Organic Photovoltaic Materials: Douala Case Study

Organic photovoltaic (OPV) materials are promising candidates for distributed solar energy in tropical regions, yet existing virtual screening tools report static power conversion efficiency (PCE) values at standard testing conditions (STC) that fail to capture the temperature-driven performance degradation experienced under real deployment conditions. Here we introduce a Climate-Native computational framework that forecasts the annual PCE profile of OPV donor molecules under geographically realistic operating conditions. The framework combines GFN2-xTB molecular dynamics with an equivariant graph neural network surrogate ($268$ Neyman-stratified CEP molecules; $120,600$ training geometries; $\sim 1050\times$ speedup over explicit quantum chemistry) and sequential deep learning models trained on annual time series anchored in NASA POWER climate data for Douala, Cameroon, and validated by zero-shot transfer to Yaoundé and Maroua. Applied to $\sim 30,000$ molecules from the Harvard Clean Energy Project (CEP) and validated against $350$ HOPV15 experimental device measurements, the framework demonstrates that sequential models trained on full molecular dynamics trajectories outperform time-averaged baselines ($35\%$-$48\%$ relative MAE improvement over static baselines), confirming that thermal conformational dynamics carry information beyond mean geometry. We further introduce a seasonal stability score that reranks OPV candidates by performance consistency under tropical conditions, identifying molecules whose deployment suitability differs substantially from their static PCE ranking.

cond-mat.mtrl-sci↗

arXiv2026

Selective Vibronic Excitation for Coherent Energy Transport in Photosynthetic and Agrivoltaic Systems

Partitioning the photonic environment into resonant and off-resonant modes provides a mechanism for dephasing suppression in photosynthetic energy transfer. Aligning the excitation spectrum with underdamped vibronic resonances in the Fenna-Matthews-Olson (FMO) complex prepares vibronically dressed states with reduced coupling to dissipative fluctuations, inducing a biexponential coherence decay: a rapid initial dephasing (τfast {\approx} 37fs) followed by persistent inter-band coherences extending beyond 1ps-a > 3x extension of the effective coherence window relative to broadband excitation (τc = 280fs). This improves forward transfer yields by 39% at 295K. PT-HOPS/SBD simulations establish that dual-band filtering at 750nm and 820nm targets vibronic resonances while bypassing dephasing-dominated noise. This enhancement is robust against static disorder (σ = 50 cm-1), with an ensemble-averaged increase of η = 0.39 {\pm} 0.04. These results identify selective vibronic excitation as a foundational design principle for coherence-assisted transport. This framework extends to symbiotic agrivoltaic systems, where organic photovoltaics function as active spectral filters to co-optimize excitonic transport alongside the photosynthetic requirements of underlying crops.

quant-ph↗

arXiv2026

Computational discovery of bifunctional organic semiconductors for energy and biosensing

The discovery of synthetically accessible organic semiconductors with exceptional performance remains a critical bottleneck in materials science. While these materials offer compelling advantages - structural modularity, mechanical flexibility, and cost-effective solution processing - for applications in photovoltaics and biosensors, identifying candidates that balance high efficiency with practical synthesis presents significant challenges. To address this challenge, we developed a high-throughput screening approach using 17 458 molecules from the PubChemQC B3LYP/6-31G*//PM6 dataset. Our strategy employs a composite metric, PCESAScore = PCE - SAScore, which systematically balances power conversion efficiency (PCE) predictions from the Scharber model against synthetic accessibility scores. This approach successfully identified seven multi-functional candidates that demonstrate both exceptional photovoltaic performance (PCE up to 36.1 %) and strong protein-binding affinity for biosensing applications. Notably, molecule 4550 emerged as the optimal candidate, exhibiting a ligand efficiency of 0.340 kcal/mol/heavy atom with 100 % target promiscuity. Our computational framework integrates machine learning, density functional theory, and molecular docking to bridge the gap between theoretical performance and experimental feasibility. These findings establish a systematic pathway for discovering synthetically compatible organic semiconductors that can simultaneously address energy conversion and molecular recognition challenges.

cond-mat.mtrl-sci↗

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↗