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AI for Property Modeling, Solvent Tailoring, and Process Design

Yuqiu Chen

University of Delaware, Department of Chemical and Biomolecular Engineering, 150 Academy Street, Newark, DE 19716, USA

1.1 AI-Assisted Property Modeling

The accurate prediction of physical properties is critical for the successful application of both chemicals and materials across various industries. Property modeling using artificial intelligence (AI) has emerged as a powerful and efficient approach in various scientific and engineering disciplines [1]. This methodology leverages advanced algorithms and machine learning (ML) techniques to predict and model the physical and chemical properties of materials, compounds, or systems [2]. One of the key advantages of employing AI in property modeling is its ability to handle complex relationships and patterns within large datasets, leading to more accurate predictions. ML models, such as neural networks and support vector machines (SVMs), can be trained on diverse datasets, enabling them to capture intricate correlations that traditional modeling methods might overlook [3]. In the field of materials science, AI-driven property modeling has proven valuable for predicting properties like conductivity, thermal conductivity, and mechanical strength [4]. Similarly, in chemistry, AI has been applied to forecast molecular properties, solubility, and reaction outcomes [5]. Given the broad scope of AI techniques, the author has chosen to focus on their application in property modeling, solvent tailoring, and process design for systems involving ionic liquids (ILs) and aqueous two-phase systems (ATPSs).

ILs are innovative fluids that have garnered significant attention from both academia and industries [6]. Over the past few decades, extensive research has been conducted on the applications of ILs in various fields such as electrochemistry [7], synthetic materials [2, 8], and pharmaceutical manufacturing [9, 10]. In these areas, ILs serve different roles, including as extractants/absorbents in separations, media and/or catalysts in lignocellulosic biomass pretreatment, and functional materials in batteries. With the growing interest in ILs within industrial settings, it is crucial to deeply understand their property behaviors for effective product and process design. For example, viscosity is a crucial transport property that plays a significant role in fluid flow analysis, process optimization, and material characterization [11]. Understanding and controlling viscosity enable engineers, researchers, and industries to design efficient systems, optimize processes, develop high-quality products, and ensure the desired performance and functionality of materials and fluids in a wide range of applications. Another example is that heat capacity is crucial for thermodynamic calculations, energy balance analyses, temperature control systems, material selection, process optimization, and fundamental research. It plays a crucial role in various scientific, engineering, and industrial applications, enabling the efficient and effective design, operation, and optimization of processes involving heat transfer and energy conversion [12]. Consequently, the characterization of IL properties and the establishment of structure–property relationships for ILs and IL-based mixtures are equally important in investigating their applications.

ATPSs, also known as aqueous biphasic systems (ABSs), are formed when two or more water-soluble components, such as polymers, salts, ILs, alkaline, and alcohols, are mixed in water at appropriate concentrations and temperatures [13]. To date, various combinations of phase-forming agents (e.g. polymer–salt/alkaline, polymer–polymer, ionic liquid–salt, and alcohol–salt) have been proposed for the creation of ATPSs [14]. Many ATPSs and the combination of these two-phase systems with other techniques such as microfluidic apparatus [15], have exhibited great technical and economic advantages in biotechnological applications [16]. Due to the high water content in both phases, ATPSs can provide a biocompatible and nondenaturing environment for cells, proteins, and other biomolecules. Meanwhile, ATPSs generally present less damage to the extracted biomolecules as they allow rapid phase separation and compound partition, leading to much lower interfacial stress than that of organic-water solvent systems. In addition, ATPSs can offer high recovery percentages and high purity of biomolecules in a one-step process. Besides these, ATPSs show characteristics of high tailored space, and they are also easy to scale up [17]. A good understanding of the mechanism that can guide the phase formation of ATPS is obviously of great importance for enhancing the opportunity for ATPS adoption in the industry. Meanwhile, the ability to provide reliable predictions on the partition of biomolecules in ATPS is also essential, given the fact that it would largely reduce the time and cost to find high-performance ATPS for biomolecules. Therefore, a systematic modeling study on the phase equilibria behavior of ATPS and the partition of biomolecules in ATPS is highly desirable for the transition of ATPS separation technique from pure academic focus to industrial implementation.

Due to the high complexity of ATPS and IL-involved systems, empirical correlations and theory-driven models cannot simultaneously provide reliable physical and thermodynamic predictions. In this respect, AI techniques such as ML algorithms are potential alternatives to model thermodynamic and transport properties of complex systems [18] such as IL-based ATPS and ionic liquid–water mixtures. The advantages of using AI techniques for modeling complex systems include the following: (i) AI techniques, particularly ML, can handle complex patterns and nonlinear relationships in data, making them well-suited for modeling intricate chemical properties. (ii) AI models can provide accurate predictions for various chemical properties. This accuracy is crucial in industries such as pharmaceuticals, materials science, and environmental research, where precise chemical property information is essential. (iii) Chemical data often involves a high number of dimensions, with numerous variables influencing the properties of interest. AI algorithms, especially those used in deep learning, excel at extracting relevant features from high-dimensional datasets. (iv) AI techniques facilitate the integration of diverse data types, including experimental results, literature data, and computational simulations. This comprehensive approach enhances the accuracy and reliability of chemical property models. (v) The increasing availability of large datasets in chemistry, including databases of chemical compounds and their properties, aligns well with AI’s ability to handle big data. AI can uncover meaningful patterns and correlations within massive datasets. (vi) AI techniques can be applied across a range of chemical domains from organic chemistry to materials science. Their adaptability makes them versatile tools for modeling diverse chemical properties.

Recently, ML algorithms, including multiple linear regression (MLR), k-nearest neighbor (KNN), decision tree (DT), random forest (RF), gradient boosting (GB), artificial neural network (ANN), multilayer perceptron (MLP), SVM, XGBoost, and lightweight gradient-boosting machine (LightGBM), have been employed as property prediction tools for complex systems. The advantages and disadvantages of these ML algorithms are described in the following text:

MLR is a supervised ML algorithm used for predicting the numerical value of a dependent variable based on the values of two or more independent variables. It is a foundational and widely used technique, especially when dealing with datasets where the outcome is influenced by multiple factors. MLR serves as the basis for more advanced regression techniques in ML.

The KNN algorithm is a versatile and intuitive ML algorithm used for both classification and regression tasks. KNN is a valuable tool in various applications, including classification problems in image recognition, recommendation systems, and anomaly detection. KNN is easy to understand and implement, making it a good choice for initial exploration of a dataset. It does not involve a training phase in the traditional sense, and the model is trained during the prediction phase. KNN can be applied to various types of datasets and is effective in capturing complex patterns. However, KNN may struggle with imbalanced datasets, where one class significantly outnumbers the others. In addition, calculating distances between data points for large datasets can be computationally intensive. Nonetheless, its simplicity and ease of implementation make it a valuable tool, especially in cases where interpretability and explainability are important.

Decision tree is a versatile and widely used ML algorithm that can be applied to both classification and regression tasks. It is a supervised learning algorithm that recursively partitions the data into subsets based on the features, ultimately leading to a decision or prediction. Decision trees are easy to interpret and visualize. The decision-making...