在多通道MolOrb图像中编码孔粒子信息,用于大型光功能材料的机器学习激发状态能量
Ziyong Chen1, Vivian Wing-Wah Yam1,2
1Institute of Molecular Functional Materials and Department of Chemistry, The University of Hong Kong, Pokfulam Road, Hong Kong, China.
Journal of the American Chemical Society
|October 24, 2023
概括
我们开发了新的分子轨道图像 (MolOrbImages) 用VGG机器学习模型预测激发状态能量. 这种方法可以实现各种分子的高精度,接近三重状态的化学精度.
科学领域:
- 计算化学
- 量子化学
- 化学中的机器学习
背景情况:
- 对分子激发状态的准确预测对于理解光物理过程至关重要.
- 现有的方法通常与复杂系统的计算成本或准确性作斗争.
研究的目的:
- 介绍一种新型的分子轨道图像 (MolOrbImages),用于预测激发状态的能量.
- 开发和验证使用这些MolOrbImages的VGG类型机器学习模型.
主要方法:
- 一个电子多通道分子轨道图像 (MolOrbImages) 的表示.
- 使用VGG类型的机器学习架构.
- 整合孔和粒子状态作为MolOrbImages中的通道.
主要成果:
- 在低的单元和三元激发状态下取得了优异的预测准确性.
- 已证明的低平均绝对误差 (MAE) <0.08 eV (QM9) 和<0.1 eV (大分子).
- 在T1状态下表现出异常性能 (MAE<1kcal/mol),接近化学精度.
结论:
- 开发的MolOrbImages和VGG模型为激发状态能量预测提供了强大的非系统特定方法.
- 物理上的洞察力可以提高模型的性能,并减少过度匹配.
- 为设计未来基于轨道的分子激发状态描述器提供指导方针.
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