Aug 9, 2026 · cond-mat.mtrl-sciJ/K move · Enter open · S save
Steve Cabrel Teguia Kouam, Rockefeller Rockefeller, Raoult Dabou Teukam, Jean-Pierre Tchapet Njafa+3
Department of Physics, Faculty of Science, University of Douala, Douala, PO. Box 24157, Littoral, Cameroon. · Division of Applied Mathematics, Faculty of Science, Stellenbosch University, Stellenbosch, South Africa. · Department of Engineering, University of Quebec in Abitibi-T´emiscamingue, Quebec, Canada. · Department of Physics, Faculty of Science, University of Yaounde 1, Yaounde, PO. Box 812, Center, Cameroon.
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;
∼1050× 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
∼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.