SFMMoE:一个半经验描述器增强的多专家图形神经网络,用于准确预测单点裂变性质
Jihang Zhai1, Danyang Xiong1, Yueqing Zhang1
1Shanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, Shanghai Frontiers Science Center of Molecule Intelligent Syntheses, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, 200062, China.
The journal of physical chemistry letters
|November 14, 2025
概括
我们开发了SFMMoE,一个图形神经网络,以有效地识别有机光伏的单片裂变 (SF) 材料. 这个工具准确地预测了激发状态的特性,加速了新型高性能材料的发现.
科学领域:
- 材料科学 材料科学 材料科学
- 计算化学计算化学
- 有机电子 有机电子
背景情况:
- 确定有机光伏的单片裂变 (SF) 材料是计算上昂贵的.
- 精确的兴奋状态能量对于SF材料的发展至关重要.
研究的目的:
- 开发一种具有成本效益和准确的方法来识别SF候选人.
- 为了加速有机光伏材料的虚拟选.
主要方法:
- 开发了一个名为SFMMoE的图形神经网络 (GNN).
- SFMMoE集成了一个多专家多元化 (MMoE) 架构与2-HOP消息传递.
- 它将分子图形拓与全球分子描述符融合在一起.
主要成果:
- SFMMoE同时预测了五个关键的激发状态属性,包括SF关键的热力学标准.
- 该模型在所有任务中实现了低于0.04 eV的平均平方误差.
- 它的表现优于传统的机器学习和GNN基线.
结论:
- 整合多任务学习和专家专业化可以提高激发状态能量学预测的准确性.
- SFMMoE使SF材料的大规模,低成本的虚拟选具有量子化学精度成为可能.
- 在线预测服务器可用于更广泛的访问.
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