基于先进的混合机器学习建模,用于预测不同温度下离子液体的特性
1Department of Pharmaceutical Sciences, College of Pharmacy, Umm Al-Qura University, P.O. Box 751, Makkah, Saudi Arabia. ssbawazeer@uqu.edu.sa.
机器学习模型准确地预测了离子液体的表面张力,从而降低了实验成本. 额外树木 (ET) 在绿色化学应用中表现出高精度和低错误率的卓越性能.
科学领域:
- 计算化学的计算化学
- 绿色化学 绿色化学
- 材料科学 材料科学 材料科学
背景情况:
- 离子液体对于可持续的化学和制药过程至关重要.
- 准确估计它们的物理化学性质对于成本和时间效率至关重要.
- 需要先进的计算方法来预测这些属性.
研究的目的:
- 评估用于预测离子液体表面张力的机器学习模型.
- 采用混合计算技术进行财产估计.
- 确定用于离子液体性质预测的最有效模型.
主要方法:
- 使用的决策树 (DT),随机森林 (RF) 和额外树 (ET) 机器学习模型.
- 采用和搜索 (HS) 算法进行超参数优化.
- 使用R平方和平均绝对百分比误差 (MAPE) 度量标准验证的模型.
主要成果:
- 额外的树木 (ET) 获得了最高的R平方值 (0.979).
- 在ET模型中,MAPE错误率 (2.05E-02) 是最低的.
- DT (0.894 R2,4.59E-02 MAPE) 和RF (0.945 R2,2.59E-02 MAPE) 也显示了预测能力.
结论:
- 额外树木 (ET) 是估计离子液体表面张力的最有效模型.
- 机器学习为财产预测的实验方法提供了一个可行的替代方案.
- 这种方法支持开发更绿色的化学工艺.
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