深度学习方法用于预测离子液体的表面张力
Nikhitha Gugulothu1, Mood Mohan2, Madaline R Marland1,3
1Manufacturing Science Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831-6201, United States.
Journal of chemical information and modeling
|June 6, 2025
概括
这项研究开发了两种深度学习模型来预测离子液体 (IL) 的表面张力. 这些模型准确地估计了IL表面张力,有助于快速设计工业应用的新材料.
科学领域:
- 材料科学 材料科学 材料科学
- 计算化学计算化学
- 化学工程是化学工程的重要组成部分.
背景情况:
- 离子液体 (IL) 是多功能溶剂,具有可调节的特性,对于电解质和传热液体等应用至关重要.
- 实验性确定IL特性,如表面张力,由于大量可能的组合和高成本而具有挑战性.
- 计算方法对于加速发现和设计具有所需特征的ILs至关重要.
研究的目的:
- 开发准确的深度学习 (DL) 模型,用于预测离子液体 (IL) 的表面张力.
- 在DL模型中使用简化的分子输入线输入系统 (SMILES) 表示来进行特征提取.
- 为快速选和合理设计具有特定表面张力值的IL提供计算工具.
主要方法:
- 开发两种使用 SMILES 对离子液体表示的深度学习模型.
- 训练和验证模型使用广泛的实验表面张力数据在各种温度.
- 使用R平方和根-平均平方误差指标评估模型性能.
主要成果:
- 这两种DL模型在预测IL表面张力方面都取得了很高的准确性,其R平方值为0.990.
- 这些模型显示了0.792 mN/m的低根-平方平均误差,表明与实验数据的良好一致.
- 这些模型有效地捕捉了IL分子结构和表面张力之间的关系.
结论:
- 基于SMILES表示的深度学习模型提供了一种强大而高效的方法来预测离子液体表面张力.
- 这些预测模型可以显著减少与实验表征相关的时间和成本.
- 开发的模型有助于合理设计和发现具有适合各种工业应用的表面张力特性的新型离子液体.
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