Mohan Mood, Demerdash Omar N, Simmons Blake A, Singh Seema, Kidder Michelle K, Smith Jeremy C
Biosciences Division and Center for Molecular Biophysics, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States.
Deconstruction Division, Joint BioEnergy Institute, 5885 Hollis Street, Emeryville, California 94608, United States.
ACS Omega. 2024 Apr 19;9(17):19548-19559. doi: 10.1021/acsomega.4c01175. eCollection 2024 Apr 30.
Carbon dioxide (CO) is a detrimental greenhouse gas and is the main contributor to global warming. In addressing this environmental challenge, a promising approach emerges through the utilization of deep eutectic solvents (DESs) as an ecofriendly and sustainable medium for effective CO capture. Chemically reactive DESs, which form chemical bonds with the CO, are superior to nonreactive, physically based DESs for CO absorption. However, there are no accurate computational models that provide accurate predictions of the CO solubility in chemically reactive DESs. Here, we develop machine learning (ML) models to predict the solubility of CO in chemically reactive DESs. As training data, we collected 214 data points for the CO solubility in 149 different chemically reactive DESs at different temperatures, pressures, and DES molar ratios from published work. The physics-driven input features for the ML models include σ-profile descriptors that quantify the relative probability of a molecular surface segment having a certain screening charge density and were calculated with the first-principle quantum chemical method COSMO-RS. We show here that, although COSMO-RS does not explicitly calculate chemical reaction profiles, the COSMO-RS-derived σ-profile features can be used to predict bond formation. Of the models trained, an artificial neural network (ANN) provides the most accurate CO solubility prediction with an average absolute relative deviation of 2.94% on the testing sets. Overall, this work provides ML models that can predict CO solubility precisely and thus accelerate the design and application of chemically reactive DESs.
二氧化碳（CO₂）是一种有害的温室气体，是全球变暖的主要促成因素。在应对这一环境挑战方面，一种有前景的方法是利用深共熔溶剂（DESs）作为一种生态友好且可持续的介质来有效捕获CO₂。与CO₂形成化学键的化学反应性DESs在CO₂吸收方面优于非反应性的、基于物理作用的DESs。然而，目前尚无能够准确预测CO₂在化学反应性DESs中溶解度的精确计算模型。在此，我们开发了机器学习（ML）模型来预测CO₂在化学反应性DESs中的溶解度。作为训练数据，我们从已发表的文献中收集了214个数据点，这些数据点涉及CO₂在149种不同化学反应性DESs中在不同温度、压力和DES摩尔比下的溶解度。用于ML模型的物理驱动输入特征包括σ-profile描述符，其量化了分子表面片段具有特定筛选电荷密度的相对概率，并通过第一性原理量子化学方法COSMO-RS进行计算。我们在此表明，尽管COSMO-RS并未明确计算化学反应剖面图，但源自COSMO-RS的σ-profile特征可用于预测键的形成。在所训练的模型中，人工神经网络（ANN）在测试集上提供了最准确的CO₂溶解度预测，平均绝对相对偏差为2.94%。总体而言，这项工作提供了能够精确预测CO₂溶解度的ML模型，从而加速了化学反应性DESs的设计和应用。
