使用自演变的可溶性数据库和图形神经网络设计溶剂系统.
Yeonjoon Kim1,2, Hojin Jung1, Sabari Kumar1
1Department of Chemistry, Colorado State University Fort Collins CO 80523 USA seonah.kim@colostate.edu.
Chemical science
|January 19, 2024
概括
我们开发了自我发展的可溶性数据库和图形神经网络,以提高可溶性预测的准确性. 这种综合方法提高了化学过程和材料设计的溶剂选择.
科学领域:
- 计算化学是一种计算化学.
- 机器学习 机器学习
- 化学工程是化学工程的组成部分.
背景情况:
- 准确的可溶性预测对于设计高效的化学合成和分离过程至关重要.
- 目前用于溶解性预测的机器学习模型在准确性和通用性方面存在局限性.
- 整合实验和计算数据是克服这些局限性的关键,但需要调和差异.
研究的目的:
- 开发一种创新的方法,用于创建自演变的可溶性数据库.
- 通过整合异质数据来提高可溶性模型的预测准确性和通用性.
- 将改进的模型应用于溶剂选择和属性预测中的实际问题.
主要方法:
- 实现了带有半监督的自我训练的图形神经网络,用于可溶性预测.
- 通过增加实验数据与量子力学计算来创建集成数据库,纠正差异.
- 数据集增强从11637个扩展到超过90万个数据点.
主要成果:
- 在溶解度预测中取得了高准确性 (测试组中平均绝对误差为0.2 kcal mol-1).
- 成功地将该模型应用于有机反应和分离过程中的溶剂选择.
- 准确预测了素衍生的单体和类似药物分子的分离系数,证明了在线性自由能量关系中的定量效用.
结论:
- 自主演变的可溶性数据库和图形神经网络方法显著改善了可溶性预测.
- 该方法为化学和材料科学中的溶剂选择和性质预测提供了一个强大的工具.
- 未来的工作可以将预测扩展到更复杂的化学物种和系统.
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