发表机构
University of Douala; Stellenbosch University; University of Quebec in Abitibi-Témiscamingue; University of Yaounde 1（杜阿拉大学; 斯泰伦博斯大学; 阿比蒂比-蒂米斯坎明格魁北克大学; 雅温得第一大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出气候原生计算框架，结合分子动力学与序列深度学习，预测热带地区OPV材料年PCE曲线，引入季节稳定性评分重新排序候选分子，验证了热构象动力学的额外信息价值。
AI 中文摘要
有机光伏（OPV）材料是热带地区分布式太阳能的有前景候选材料，但现有虚拟筛选工具报告的是标准测试条件（STC）下的静态功率转换效率（PCE）值，无法捕捉实际部署条件下温度驱动的性能衰减。本文引入气候原生计算框架，预测有机光伏给体分子在地理真实运行条件下的年PCE曲线。该框架结合GFN2-xTB分子动力学与等变图神经网络代理（涉及268个奈曼分层的CEP分子、120600个训练几何结构，比显式量子化学快约1050倍），以及基于喀麦隆杜阿拉NASA POWER气候数据锚定的年时间序列训练的序列深度学习模型，通过零样本迁移至雅温得和马鲁阿进行验证。将该框架应用于哈佛清洁能源项目（CEP）的约30000个分子，并通过350个HOPV15实验器件测量值验证，结果显示，在完整分子动力学轨迹上训练的序列模型优于时间平均基线（比静态基线的平均绝对误差相对降低35%-48%），证实热构象动力学携带超出平均几何结构的信息。本文还引入季节稳定性评分，按热带条件下的性能一致性对OPV候选分子重新排序，识别出部署适用性与其静态PCE排名存在显著差异的分子。
英文摘要
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.
Comments12 pages, 5 figures and 2 tables
