Exploring the Knowledge Attained by Machine Learning on Ion Transport across Polyamide Membranes Using Explainable Artificial Intelligence

Toward Molecular Simulation Guided Design of Next-Generation Membranes: Challenges and Opportunities

Membranes provide energy-efficient solutions for separating ions from water, ion-ion separation, neutral or charged molecules, and mixed gases. Understanding the fundamental mechanisms and design principles for these separation challenges has significant applications in the food and agriculture, energy, pharmaceutical, and electronics industries and environmental remediation. In situ experimental probes to explore Angstrom-nanometer length-scale and pico-nanosecond time-scale phenomena remain limited. Currently, molecular simulations such as density functional theory, ab initio molecular dynamics (MD), all-atom MD, and coarse-grained MD provide physics-based predictive models to study these phenomena. The status of molecular simulations to study transport mechanisms and state-of-the-art membrane separation is discussed. Furthermore, limitations and open challenges in molecular simulations are discussed. Finally, the importance of molecular simulations in generating data sets for machine learning and exploration of membrane design space is addressed.

Machine learning-guided performance prediction of forward osmosis polymeric membranes for boron recovery

Efficient recovery of boron is one of the crucial strategies of sustainably extracting valuable resource from water. It however still remains a key technological challenge to efficiently predict boron recovery from unconventional water resources such as underground water, geothermal water and seawater, which are still few concerned in open literature. To effectively address this issue, herein we propose an efficient strategy to precisely predict boron recovery performance and then explore mechanism in forward osmosis process via advanced machine learning techniques with better model performance. Specifically, to explore the complex relationships among various boron recovery factors, we compare different advanced machine learning regression models to provide valuable insights into how these key factors impact system performance. We find that three key driving factors (i.e., pH, boron concentration, and membrane orientation) significantly affect boron recovery performance in the forward osmosis process. The best prediction accuracy with a high r-square (R2, 95.4 %) is achieved via the XGBoost model combined with the particle swarm optimization algorithm, demonstrating its remarkable ability for precise boron recovery prediction. By employing this hybrid model to optimize the search space, the overall performance of forward osmosis system was significantly enhanced, with a predicted boron rejection rate as high as 98.28 %, outperforming the reported values. Our work demonstrates the powerful potential of advanced machine learning for efficiently predicting boron recovery for water quality improvement and resource recovery applications.

Machine learning-based prediction of non-aeration linear alkylbenzene sulfonate mineralization in an oxygenic microalgal-bacteria biofilm

Microalgal-bacteria biofilm shows great potential in low-cost greywater treatment. Accurately predicting treated greywater quality is of great significance for water reuse. In this work, machine learning models were developed for simulating and predicting linear alkylbenzene sulfonate (LAS) removal using 152-days collected data from a battled oxygenic microalgal-bacteria biofilm reactor (MBBfR). By using nine variables including influent LAS, hydraulic retention time (HRT), biofilm density and thickness, specific oxygen production and consumption rates, microalgae and bacteria concentrations, and dissolved oxygen (DO), the support vector machine (SVM) model enabled the accurate LAS removal prediction (training set: R2 = 0.995, (root mean square error, RMSE) = 0.076, (mean absolute error, MAE) = 0.069; testing set: R2 = 0.961, RMSE = 0.251, MAE = 0.153). SVM can be also successfully applied for MBBfR operation optimization (HRT = 4.28 h, DO = 0.25 mg/L) that achieving accurate prediction of LAS mineralization.

Machine Learning-Aided Inverse Design and Discovery of Novel Polymeric Materials for Membrane Separation

Polymeric membranes have been widely used for liquid and gas separation in various industrial applications over the past few decades because of their exceptional versatility and high tunability. Traditional trial-and-error methods for material synthesis are inadequate to meet the growing demands for high-performance membranes. Machine learning (ML) has demonstrated huge potential to accelerate design and discovery of membrane materials. In this review, we cover strengths and weaknesses of the traditional methods, followed by a discussion on the emergence of ML for developing advanced polymeric membranes. We describe methodologies for data collection, data preparation, the commonly used ML models, and the explainable artificial intelligence (XAI) tools implemented in membrane research. Furthermore, we explain the experimental and computational validation steps to verify the results provided by these ML models. Subsequently, we showcase successful case studies of polymeric membranes and emphasize inverse design methodology within a ML-driven structured framework. Finally, we conclude by highlighting the recent progress, challenges, and future research directions to advance ML research for next generation polymeric membranes. With this review, we aim to provide a comprehensive guideline to researchers, scientists, and engineers assisting in the implementation of ML to membrane research and to accelerate the membrane design and material discovery process.

Elucidating governing factors of PFAS removal by polyamide membranes using machine learning and molecular simulations

Per- and polyfluoroalkyl substances (PFASs) have recently garnered considerable concerns regarding their impacts on human and ecological health. Despite the important roles of polyamide membranes in remediating PFASs-contaminated water, the governing factors influencing PFAS transport across these membranes remain elusive. In this study, we investigate PFAS rejection by polyamide membranes using two machine learning (ML) models, namely XGBoost and multimodal transformer models. Utilizing the Shapley additive explanation method for XGBoost model interpretation unveils the impacts of both PFAS characteristics and membrane properties on model predictions. The examination of the impacts of chemical structure involves interpreting the multimodal transformer model incorporated with simplified molecular input line entry system strings through heat maps, providing a visual representation of the attention score assigned to each atom of PFAS molecules. Both ML interpretation methods highlight the dominance of electrostatic interaction in governing PFAS transport across polyamide membranes. The roles of functional groups in altering PFAS transport across membranes are further revealed by molecular simulations. The combination of ML with computer simulations not only advances our knowledge of PFAS removal by polyamide membranes, but also provides an innovative approach to facilitate data-driven feature selection for the development of high-performance membranes with improved PFAS removal efficiency.

Enhancing Ion Selectivity of Nanofiltration Membranes via Heterogeneous Charge Distribution

Nanofiltration technology holds significant potential for precisely separating monovalent and multivalent ions, such as lithium (Li) and magnesium (Mg) ions, during lithium extraction from salt lakes. This study bridges a crucial gap in understanding the impact of the membrane spatial charge distribution on ion-selective separation. We developed two types of mixed-charge membranes with similar pore sizes but distinct longitudinal and horizontal distributions of oppositely charged domains. The charge-mosaic membrane, synthesized and utilized for ion fractionation for the first time, achieved an exceptional water permeance of 15.4 LMH/bar and a Li/Mg selectivity of 108, outperforming the majority of published reports. Through comprehensive characterization, mathematical modeling, and machine learning methods, we provide evidence that the spatial charge distribution dominantly determines ion selectivity. The charge-mosaic structure excels by substantially promoting ion selectivity through locally enhanced Donnan effects while remaining unaffected by variations in feedwater concentration. Our findings not only demonstrate the applicability of charge-mosaic membranes to precise nanofiltration but also have profound implications for technologies demanding advanced ion selectivity, including those in the sustainable water treatment and energy storage industries.

Interfacial Polymerization of Aromatic Polyamide Reverse Osmosis Membranes

Polyamide membranes are widely used in reverse osmosis (RO) water treatment, yet the mechanism of interfacial polymerization during membrane formation is not fully understood. In this work, we perform atomistic molecular dynamics simulations to explore the cross-linking of trimesoyl chloride (TMC) and m-phenylenediamine (MPD) monomers at the aqueous-organic interface. Our studies show that the solution interface provides a function of "concentration and dispersion" of monomers for cross-linking. The process starts with rapid cross-linking, followed by slower kinetics. Initially, amphiphilic MPD monomers diffuse in water and accumulate at the solution interface to interact with TMC monomers from the organic phase. As cross-linking progresses, a precross-linked thin film forms, reducing monomer diffusion and reaction rates. However, the structural flexibility of the amphiphilic film, influenced by interfacial fluctuations and mixed interactions with water and the organic solvent at the solution interface, promotes further cross-linking. The solubility of MPD and TMC monomers in different organic solvents (cyclohexane versus n-hexane) affects the cross-linking rate and surface homogeneity, leading to slight variations in the structure and size distribution of subnanopores. Our study of the interfacial polymerization process in explicit solvents is essential for understanding membrane formation in various solvents, which will be crucial for optimal polyamide membrane design.

Mapping global distributions of clay-size minerals via soil properties and machine learning techniques

Clay-size mineral is a vital ingredient of soil that influences various environment behaviors. It is crucial to establish a global distribution map of clay-size minerals to improve the recognition of environment variations. However, there is a huge gap of lacking some mineral contents in poorly accessible remote areas. In this work, machine learning (ML) approaches were conducted to predict the mineral contents and analyze their global abundance changes through the relationship between soil properties and mineral distributions. The average content of kaolinite, illite, smectite, vermiculite, chlorite, and feldspar were predicated to be 28.69 %, 22.30 %, 12.42 %, 5.43 %, 5.03 %, and 1.44 % respectively. Model interpretation showed that topsoil bulk density and drainage class were the most significant factors for predicting all six minerals. It could be seen from the feature importance analysis that bulk density notably reflected the distribution of 2:1 layered minerals more than that of 1:1 mineral. High drainage favored secondary minerals development, while low drainage was more benefited for primary minerals. Moreover, the content variation of different minerals aligned with the distribution of corresponding soil properties, which affirmed the accuracy of established models. This study proposed a new approach to predict mineral contents through soil properties, which filled a necessary step of understanding the geochemical cycles of soil-related processes.

Machine Learning in Membrane Design: From Property Prediction to AI-Guided Optimization

Porous membranes, either polymeric or two-dimensional materials, have been extensively studied because of their outstanding performance in many applications such as water filtration. Recently, inspired by the significant success of machine learning (ML) in many areas of scientific discovery, researchers have started to tackle the problem in the field of membrane design using data-driven ML tools. In this Mini Review, we summarize research efforts on three types of applications of machine learning in membrane design, including (1) membrane property prediction using ML, (2) gaining physical insight and drawing quantitative relationships between membrane properties and performance using explainable artificial intelligence, and (3) ML-guided design, optimization, or virtual screening of membranes. On top of the review of previous research, we discuss the challenges associated with applying ML for membrane design and potential future directions.

Integrating molecular descriptors for enhanced prediction: Shedding light on the potential of pH to model hydrated electron reaction rates for organic compounds

Hydrated electron reaction rate constant (ke-aq) is an important parameter to determine reductive degradation efficiency and to mitigate the ecological risk of organic compounds (OCs). However, OC species morphology and the concentration of hydrated electrons (e-aq) in water vary with pH, complicating OC fate assessment. This study introduced the environmental variable of pH, to develop models for ke-aq for 701 data points using 3 descriptor types: (i) molecular descriptors (MD), (ii) quantum chemical descriptors (QCD), and (iii) the combination of both (MD + QCD). Models were screened using 2 descriptor screening methods (MLR and RF) and 14 machine learning (ML) algorithms. The introduction of QCDs that characterized the electronic structure of OCs greatly improved the performance of models while ensuring the need for fewer descriptors. The optimal model MLR-XGBoost(MD + QCD), which included pH, achieved the most satisfactory prediction: R2tra = 0.988, Q2boot = 0.861, R2test = 0.875 and Q2test = 0.873. The mechanistic interpretation using the SHAP method further revealed that QCDs, polarizability, volume, and pH had a great influence on the reductive degradation of OCs by e-aq. Overall, the electrochemical parameters (QCDs, pH) related to the solvent and solute are of significance and should be considered in any future ML modeling that assesses the fate of OCs in aquatic environment.