Designing Chemical Processes with Multicomponent Solvents through Self-Evolving Solubility Databases and Neural Networks

在化学系化学理论、模型和计算方法（CTMC）项目的支持下，科罗拉多州立大学化学系的Seonah Kim正在开发复杂多组分体系中溶解度的新型机器学习（ML）预测模型。溶解度对于设计各种化学品至关重要，如药物，可再生燃料，电解质和聚合物。此外，溶解度在用于获得这些化学品的合成和分离过程中也起着关键作用。在这个项目中，Kim团队将从计算和实验数据中构建一个广泛的自进化溶解度数据库，并使用图神经网络（GNN）开发ML溶解度预测模型，以实现更快速和准确的溶解度预测。这种方法的一个新的创新将扩展到多组分溶剂系统和聚合物溶解度。该项目将为研究生和本科生提供一个获得现代科学企业各种相关技能的机会。此外，Kim研究团队将积极参与外展活动，与当地学校建立联系，并通过面对面和虚拟活动接触全国观众。拟议的研究将首先开发创新的自进化溶解度数据库和GNN。该方法将采用半监督的自我训练，结合实验和计算数据库，以纠正这两个数据源之间的差异。这种方法的一个独特之处是在整合到数据库之前，对量子化学（QC）方法计算的溶解度进行校正。 将对广泛范围的溶质和溶剂进行该数据扩充过程。将采用多种QC方法，例如导体样筛选模型（COSMO）和密度泛函理论（DFT）以及基于密度的隐式溶剂化模型（SMD），以实现可靠的增强。然后，自我培训计划将扩大到多组分系统。将通过结合多个组分之间复杂相互作用的描述符来检查各种GNN变体，并将利用该模型来生成SMD的新溶剂参数。聚合物溶解度的预测将通过实验验证作为另一个实际应用。Seonah Kim博士的研究小组将为各种溶质（包括溶解在单一和多组分溶剂中的聚合物）开发广泛的、自我进化的、可靠的溶解度数据库和准确的溶解度预测GNN。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估而被认为值得支持。

With support from the Chemical Theory, Models and Computational Methods (CTMC) program in the Division of Chemistry, Seonah Kim of the Department of Chemistry at Colorado State University is developing novel machine-learning (ML) predictive models for solubility in complex multicomponent systems. Solubility is essential to designing various chemicals such as pharmaceutical drugs, renewable fuels, electrolytes, and polymers. Further, solubility also plays a key role in the synthesis and separation processes employed to obtain these chemicals. In this project, the Kim group will build an extensive self-evolving solubility database from computational and experimental data and develop an ML solubility prediction model using graph neural networks (GNNs) for more rapid and accurate solubility predictions. A novel innovation in this approach will be expansion to multicomponent solvent systems and polymer solubility. The project will offer graduate and undergraduate students an opportunity to acquire a diverse range of relevant skills for the modern scientific enterprise. Additionally, the Kim research team will actively participate in outreach events, connecting with local schools and reaching a nationwide audience through in person and virtual activities. The proposed research will first develop innovative self-evolving solubility databases and GNNs. The approaches will adopt semi-supervised self-training, which combines experimental and computational databases to rectify the discrepancies between these two data sources. A unique aspect of this approach will be the correction of solubilities calculated from quantum chemistry (QC) methods prior to integration into the database. This data augmentation process will be performed for a broad scope of solutes and solvents. Multiple QC methods will be employed, such as the Conductor-like Screening Model (COSMO) and density functional theory (DFT) with an implicit Solvation Model based on Density (SMD), for a reliable augmentation. The self-training scheme will then be expanded to multicomponent systems. Various GNN variants will be examined by incorporating the descriptors for complex interactions among multiple components and the model will be leveraged to generate new solvent parameters for SMD. Prediction of polymer solubility will be followed by experimental validation as another practical application. Dr. Seonah Kim’s group will develop extensive, self-evolving, and reliable solubility databases and accurate solubility prediction GNNs for a wide range of solutes, including polymers dissolved in single and multicomponent solvents.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.