通过可溶性预测推进能量储存:利用深度学习的潜力
Mesfin Diro Chaka1,2, Yedilfana Setarge Mekonnen3, Qin Wu4
1Department of Physics, College of Natural and Computational Sciences, Addis Ababa University, P. O. Box 1176, Addis Ababa, Ethiopia. mesfin.diro@aau.edu.et.
Physical chemistry chemical physics : PCCP
|November 15, 2023
概括
这项研究使用MolGAT图形神经网络模型准确预测有机分子可溶性,用于储能. 该方法成功识别了超过12,000个可溶性,氧化还原活性分子,用于先进的电池应用.
科学领域:
- 材料科学 材料科学 材料科学
- 计算化学计算化学
- 电化学 电化学 电化学
背景情况:
- 溶解度预测对于开发高效可靠的储能系统,特别是氧化还原流电池至关重要.
- 预测溶解度的传统方法通常涉及量子计算或基于描述者的方法.
- 机器学习,特别是图形神经网络 (GNN),显示出对精确建模复杂的分子结构-属性关系的巨大潜力.
研究的目的:
- 应用和验证MolGAT GNN模型用于预测有机分子的水溶性.
- 选大量的氧化还原活性有机化合物的数据库,以寻找与储能相关的有利可溶性特性.
- 开发一个强大的,两相的虚拟选策略,用于识别水性氧化还原流电池的新材料.
主要方法:
- 能够处理复杂分子图形特征的MolGAT模型,是一种GNN,在AqSolDB数据库中的8494个有机分子上进行了训练.
- 该模型的可溶性预测准确性与现有的最先进的基于图形和基于描述器的模型进行了比较.
- 实施了两阶段的高通量虚拟选过程:首先是氧化还原潜力,然后是水溶性.
主要成果:
- 与其他先进模型相比,MolGAT模型在预测水溶性方面表现出卓越的准确性.
- 最初的选发现了23467个有前途的氧化还原活性分子.
- 随后的溶解性选确定了12332个适合水性氧化还原流电池的氧化还原活性和可溶性有机分子.
结论:
- 摩尔GAT模型是一个可靠的工具,用于预测各种有机分子的可溶性.
- 开发的双相选策略有效地从大型数据集中识别出内在可溶性,氧化还原活性分子.
- 这种方法加速了用于储能的先进材料的发现,并为制药,环境和化学应用提供了洞察力.
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