基于机器学习和验证的计算模型用于预测混合物中的离子液体粘度
Bader Huwaimel1,2, Jowaher Alanazi3, Muteb Alanazi4
1Department of Pharmaceutical Chemistry, College of Pharmacy, University of Ha'il, Hail, 81442, Saudi Arabia. b.huwaimel@uoh.edu.sa.
机器学习模型使用阳离子,阳离子,温度和度准确预测离子液溶液粘度. 随机森林,梯度提升和XGBoost模型表现出高预测准确度,随机森林实现了0.9971.0的R2.
科学领域:
- 物理化学 物理化学
- 计算化学的计算化学
- 材料科学 材料科学 材料科学
背景情况:
- 离子液体是具有调节性质的多功能溶剂.
- 准确的粘度预测对于它们在各种化学过程中的应用至关重要.
- 现有的模型可能缺乏复杂的离子液体溶液所需的精度.
研究的目的:
- 开发和评估用于预测离子液体溶液粘度的机器学习模型.
- 根据关键参数确定用于粘度估计的最有效算法.
- 使用超参数调来优化模型性能.
主要方法:
- 使用随机森林 (RF),梯度提升 (GB) 和XGBoost (XGB) 机器学习算法.
- 输入参数包括阴离子类型,离子类型,温度 (K) 和离子液度 (mol%).
- 采用Glowworm Swarm优化 (GSO) 来进行超参数优化.
主要成果:
- 随机森林 (RF) 实现了最高的预测准确度,R2为0.9971.
- 梯度提升 (GB) 和XGBoost (XGB) 也表现出高性能,其R2值分别为0.9916和0.9911.
- 模型与实验数据有很好的一致性,并通过RMSE和MAPE指标进一步验证.
结论:
- 机器学习模型,特别是随机森林模型,对于预测离子液溶液粘度非常有效.
- 选择的输入参数和优化技术显著提高预测能力.
- 这些模型为设计和应用离子液体提供了可靠和高效的工具.
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