基于物理的机器学习模型预测了二氧化碳在化学反应性深层阳性溶剂中的可溶性
Mood Mohan1, Omar N Demerdash1, Blake A Simmons2,3
1Biosciences Division and Center for Molecular Biophysics, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States.
ACS omega
|May 6, 2024
概括
机器学习模型准确地预测了二氧化碳 (CO2) 在化学反应性深层性溶剂 (DES) 中的溶解度. 这一突破加速了使用这些环保溶剂的可持续二氧化碳捕获技术的发展.
科学领域:
- 环境化学环境化学
- 计算化学计算化学
- 材料科学 材料科学 材料科学
背景情况:
- 二氧化碳 (CO2) 是推动全球变暖的主要温室气体.
- 深度环氧溶剂 (DES) 提供了可持续和环保的二氧化碳捕获媒介.
- 与非反应性DES相比,化学反应性DES显示出更高的二氧化碳吸收能力,但缺乏精确的可溶性预测模型.
研究的目的:
- 开发精确的机器学习 (ML) 模型,用于预测化学反应性DES中的二氧化碳可溶性.
- 为了利用从量子化学方法获得的物理驱动的输入特征.
- 为了能够精确预测二氧化碳的溶解性,促进先进的二氧化碳捕获材料的设计.
主要方法:
- 从已发表的文献中收集了149种化学反应性DES中的二氧化碳溶解度的214个数据点.
- 使用通过COSMO-RS方法计算的s-profile描述符,作为ML模型的物理驱动输入特征.
- 训练和评估各种ML模型,包括人工神经网络 (ANN).
主要成果:
- 开发的ML模型,特别是ANN,在预测二氧化碳溶解度方面表现出高准确性.
- 性能最好的ANN模型在测试数据集上实现了2.94%的平均绝对相对偏差.
- 来自COSMO-RS的s-profile特征在预测键形成方面被证明是有效的,尽管它没有明确地建模反应配置文件.
结论:
- 机器学习模型可以准确预测化学反应性DES中的二氧化碳溶解度.
- 开发的模型可以显著加速有效的基于DES的二氧化碳捕获系统的设计和应用.
- 这项工作为推进可持续的二氧化碳捕获战略提供了有价值的计算工具.
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