使用机器学习的距离隔离函数来精确对有机双基的电子和光学性能
Cheng-Wei Ju1,2, Yili Shen3,4, Ethan J French1,5,6
1Department of Chemistry, University of Massachusetts, Amherst, Massachusetts 01003, United States.
The journal of physical chemistry. A
|February 21, 2024
概括
机器学习准确地预测有机基的光学特性,将其应用扩展到这些独特的半导体材料. 这种方法提供了显著的计算节约,同时保持了对吸收和光能量的高精度.
科学领域:
- 计算化学计算化学
- 材料科学 材料科学 材料科学
- 有机电子 有机电子
背景情况:
- 有机半导体双旋转基具有独特的光学特性,由于最小的旋转翻转系统间交叉 (ISC).
- 它们的多配置性质对传统的单参考密度函数理论 (DFT) 计算提出了挑战.
研究的目的:
- 扩展ML-ωPBE功能,使用堆叠组合机器学习 (SEML) 开发,以准确计算双旋转有机基的特性.
- 评估ML-ωPBE的性能,以预测像吸收和光能量这样的光学特性.
主要方法:
- 将ML-ωPBE区间分离混合 (RSH) 交换相关 (XC) 函数扩展到64个双旋转基的数据集.
- 使用了一套新的训练套件,包括3926个封闭分子和64个激素.
- 使用线性响应时间依赖的DFT (TDDFT) 来评估吸收 (Eabs) 和光 (Efl) 能量,将ML-ωPBE与其他九个XC函数进行比较.
主要成果:
- ML-ωPBE准确预测了分子依赖范围分离参数 (ω) 的小平均绝对误差 (MAE) 为0.0197 a0−1,与OT-ωPBE相比,但计算成本显著降低 (2.46级小).
- 证明了ML-ωPBE对各种有机半导体物种的优秀域适应性.
- ML-ωPBE重现了实验吸收和光能量,其MAE分别为0.299 eV和0.254 eV,与OT-ωPBE的性能非常相匹配.
结论:
- 成功地将SEML框架和ML-ωPBE功能从封闭外分子扩展到双旋转有机基.
- ML-ωPBE提供了一个计算效率高,准确的方法,用于计算有机半导体的光学特性,使用单一参考TDDFT.
- 这项工作为新兴光学材料的计算研究开辟了新的途径,例如有机自旋基.
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