arXiv Machine Learning August 13, 2026 By Steve Cabrel Teguia Kouam, Rockefeller Rockefeller, Raoult Dabou Teukam, Jean-Pierre Tchapet Njafa, Patrick Sorrel Mvoto Kongo, Jean-Pierre Nguenang, Serge Guy Nana Engo

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

Read the original on arXiv Machine Learning →

arXiv:2608. 11261v1 Announce Type: cross Abstract: 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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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