Surfactant representation using COSMO screened charge density for adsorption isotherm prediction using Physics-Informed Neural Network (PINN)

IF 5.7 2区 化学 Q1 CHEMISTRY, MULTIDISCIPLINARY
Achmad Anggawirya Alimin, Kattariya Srasamran, Wanutchaya Yuenyong, Ampira Charoensaeng, Bor-Jier Shiau, Uthaiporn Suriyapraphadilok
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引用次数: 0

Abstract

Predicting surfactant adsorption using the currently available isotherm model is limited to one or two independent variables: equilibrium concentration and temperature. This study aims to develop an adsorption model that includes molecular features, testing conditions, and solid properties in the model. A Physics-Informed Neural Network (PINN) was structured by integrating adsorption isotherm into artificial neural networks (ANN). The model was trained using a dataset containing 56 adsorption isotherms and 20 types of anionic and nonionic surfactants under various conditions with sand and silica oxide as their solids. The surfactants were quantified using sets of descriptors generated from molecular counting, charge distribution, and Conductor-like Screening Model (COSMO) screened charge density. The COSMO-screened charge density descriptors provide the highest accuracy in representing the surfactant molecule. The interpretation of molecular structure effect and surfactant-solid interaction described using COSMO-screened charge density showed that adsorption between the surfactant and solid media involves hydrogen bonding and hydrophobic interaction. The PINN model achieves high accuracy with 93% training and 85% validation with fivefold cross-validation. Later, the model was evaluated and used to generate an adsorption isotherm and predict unseen surfactant adsorption. Adsorption prediction with unseen surfactants showed high accuracy with the surfactant for familiar structure (RMSE 0.07 mg/g) and promising profile for the whole new structure (RMSE 2.95 mg/g). Scientific contribution This study advances the field by integrating COSMO-screened charge density descriptors into a physics-informed deep learning model to predict surfactant adsorption isotherms, accounting for molecular features, testing conditions, and solid properties. The incorporation of COSMO-screened charge density offers a novel approach to accurately represent surfactant molecules, enabling accurate prediction of their adsorption behavior. This approach extends conventional models, which are often limited to empirical parameters or fewer variables. This physics-informed framework significantly enhances the understanding of surfactant-solid interactions and offers a robust predictive tool for optimizing surfactant formulations, aiming to minimize adsorption losses in chemical enhanced oil recovery and environmental remediation.

基于COSMO筛选电荷密度的表面活性剂表征用于物理信息神经网络(PINN)吸附等温线预测
使用目前可用的等温线模型预测表面活性剂吸附仅限于一个或两个独立变量:平衡浓度和温度。本研究旨在建立一个包括分子特征、测试条件和模型中固体性质的吸附模型。将吸附等温线与人工神经网络相结合,构建了物理信息神经网络(PINN)。该模型使用包含56条吸附等温线和20种阴离子和非离子表面活性剂的数据集进行训练,这些表面活性剂在不同条件下以砂和氧化硅为固体。利用分子计数、电荷分布和类导体筛选模型(COSMO)筛选的电荷密度生成的描述符集对表面活性剂进行量化。cosmo筛选的电荷密度描述符在表示表面活性剂分子方面提供了最高的准确性。利用cosmo筛选电荷密度对分子结构效应和表面活性剂-固体相互作用的解释表明,表面活性剂与固体介质之间的吸附涉及氢键和疏水相互作用。通过5倍交叉验证,该模型达到了93%的训练精度和85%的验证精度。随后,对该模型进行了评估,并用于生成吸附等温线和预测未知表面活性剂的吸附。未知表面活性剂的吸附预测结果表明,对于已知结构的表面活性剂(RMSE为0.07 mg/g)具有较高的预测精度,对于全新结构的表面活性剂(RMSE为2.95 mg/g)具有较好的预测前景。该研究通过将cosmos筛选的电荷密度描述符整合到物理信息深度学习模型中,以预测表面活性剂吸附等温线,考虑分子特征、测试条件和固体性质,从而推进了该领域的发展。cosmo筛选电荷密度的结合提供了一种准确表征表面活性剂分子的新方法,从而能够准确预测其吸附行为。这种方法扩展了通常限于经验参数或较少变量的传统模型。这一物理知识框架显著增强了对表面活性剂-固体相互作用的理解,并为优化表面活性剂配方提供了强大的预测工具,旨在最大限度地减少化学提高采收率和环境修复中的吸附损失。
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来源期刊
Journal of Cheminformatics
Journal of Cheminformatics CHEMISTRY, MULTIDISCIPLINARY-COMPUTER SCIENCE, INFORMATION SYSTEMS
CiteScore
14.10
自引率
7.00%
发文量
82
审稿时长
3 months
期刊介绍: Journal of Cheminformatics is an open access journal publishing original peer-reviewed research in all aspects of cheminformatics and molecular modelling. Coverage includes, but is not limited to: chemical information systems, software and databases, and molecular modelling, chemical structure representations and their use in structure, substructure, and similarity searching of chemical substance and chemical reaction databases, computer and molecular graphics, computer-aided molecular design, expert systems, QSAR, and data mining techniques.
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