1. Introduction
Membrane separation technology has become a cornerstone of modern industrial processes, playing an indispensable role in driving energy efficiency and sustainable chemical engineering. From the earliest observation of osmosis to the large-scale industrial application of advanced membranes, membrane science has undergone a long and tortuous evolution, closely intertwined with progress in materials science, chemical engineering, and environmental science [
1]. In the 21st century, membrane technology has entered a period of diversified development, with remarkable progress in the fields of both organic and inorganic membranes. Globally, membrane applications have expanded from traditional water treatment and gas separation to new fields such as biomedicine, electronic chemicals, and new energy. Despite these significant advancements, high-performance membrane development is still bottlenecked by intuition-driven, trial-and-error methods, where material performance is only assessed after actual synthesis [
2], [
3]. This traditional empirical paradigm is fundamentally inadequate for navigating the combinatorial explosion inherent in modern materials design. For example, the theoretical design space for potential polymeric repeat units and metal-organic framework (MOF) structures far exceeds the capacity of manual screening.
Driven by the growing demand for high-efficiency, sustainable, and multifunctional separation technologies, the concept of “future membranes” has recently been proposed [
4]. Rather than merely being made from upgraded existing materials, future membranes are envisioned as intelligent integrated systems, and artificial intelligence (AI) is emerging as the central driving force of this transformative shift [
5]. By overcoming the inherent limitations of trial-and-error exploration in vast chemical and structural spaces, AI is fundamentally reshaping membrane science across multiple scales (
Fig. 1). At the atomic/molecular scale (∼10
−10 m), it enables high-throughput materials screening; at the nanoscale (∼10
−9 m), it facilitates the inverse design of nanochannels and unravels confined transport mechanisms governing separation behavior; at larger engineering scales (∼10
−3-10
2 m), it drives the optimization of intelligent membrane module design and supports accurate membrane-fouling prediction. Finally, at the industrial scale (>100 m), AI underpins intelligent operating systems for large-scale membrane processes. Thus, AI is catalyzing a comprehensive paradigm shift in membrane science, bridging fundamental laboratory research and applied industrial engineering.
2. Key challenges in AI-empowered separation membranes
AI-empowered separation membrane technology represents the frontier of advanced chemical engineering and green separation. By integrating advanced computational technologies with physicochemical insights into materials, it formulates customized membrane design strategies for specific application scenarios and significantly pushes the boundaries of performance limits. Through the analysis of massive, heterogeneous datasets (e.g., computational materials databases, experimental characterizations, and operational conditions), AI models can uncover and generate previously unrecognized deep structure-property relationships, precisely predict membrane separation efficacy and fouling evolution, and accelerate the research and development of novel high-performance membrane materials.
The application of AI to separation membrane science remains at an early exploratory stage, with the comprehensive unleashing of AI’s potential constrained by critical bottlenecks. These include the inherent physicochemical complexity of real-world multicomponent separation systems, the limited interpretability and generalizability of existing “black-box” algorithms, and the formidable challenges of translating laboratory-scale models into viable, large-scale industrial applications. The key challenges currently presented by the AI-driven design and application of separation membranes are discussed below.
2.1. Data scarcity and lack of standardization
The predictive capability and accuracy of AI models are fundamentally constrained by the quality and scale of the models’ training data. In membrane science, most existing data are unstructured and scattered across extensive literature, leading to issues of fragmentation, incompleteness, and a lack of standardized reporting protocols (
Fig. 2). More importantly, the vast majority of published experimental data only comprise carefully curated positive results selected by researchers. By contrast, the training of robust AI models equally demands a large volume of negative or failed experimental data. Acquiring such unpublished data, however, requires extensive collaboration across research groups, universities, and institutions, which is a major challenge complicated by competing interests and confidentiality constraints. Compounding these issues, diverse characterization techniques (e.g., spectroscopy, electrochemical profiles, and microscopic images) generate multi-source heterogeneous data. Although computational simulations provide a vital means of dataset construction, fundamental computational data for membrane materials remain highly inconsistent: Different studies often adopt disparate parameters such as functionals, cutoff energies,
k-point grids, and force fields. The data-silo effect induced by these factors further reduces available data and exacerbates data scarcity, preventing AI readiness and fundamentally hindering the reliable training of large-scale, general-purpose AI models.
To practically navigate these barriers and move toward shared community databases, the implementation of federated learning (FL) architectures presents a viable strategy. FL enables multiple institutions to collaboratively train general-purpose AI models by sharing only updated model weights (gradients) rather than raw proprietary data, thereby improving model transferability while strictly preserving institutional privacy [
6]. In addition, to address the inherent lack of standardization, it is recommended that the membrane community converge on standardized data-reporting frameworks governed by findability, accessibility, interoperability, and reusability (FAIR) principles [
7]. This requires establishing consensus-driven metadata ontologies that mandate the comprehensive reporting of exact testing conditions, synthesis parameters, and structural characterizations, which could eventually be integrated into the submission processes of leading journals as standardized benchmarks.
2.2. Lack of physical interpretability
At present, mainstream AI algorithms are predominantly pure data-driven black-box models. While these models demonstrate exceptional proficiency in fitting complex nonlinear mappings, they inherently fail to elucidate the underlying physicochemical mechanisms (
Fig. 3). This limitation becomes particularly evident in frontier interdisciplinary domains. Examples include the coupling of membrane separation with catalytic processes for reaction intensification or the elucidation of confined mass-transfer mechanisms at the sub-nanometer scale. In such complex scenarios, the absence of physical boundary constraints is a critical flaw rendering AI models highly susceptible to yielding erroneous predictions that violate fundamental thermodynamic or kinetic laws. Consequently, the development of physics-informed data-driven models that primarily ensure the physical consistency of AI predictions remains a core challenge within the field.
Moreover, validating black-box AI predictions is challenging, particularly when the underlying dataset is sparse or noisy. Under such conditions, models usually suffer from overfitting and poor generalization capabilities. Although several advanced algorithms have been employed to mitigate small-data challenges, such as few-shot learning, transfer learning, and generative data augmentation, their effectiveness in materials science and membrane science remains fundamentally limited. For instance, transfer learning is susceptible to negative transfer when the physicochemical domain shifts significantly. As a result, these algorithmic mitigation strategies cannot fully compensate for the lack of foundational data; as extensively discussed in Section 2.1, the inadequacy of high-quality data remains a critical bottleneck compromising model validation.
To establish a more rigorous and generalizable validation framework, the community should transition from algorithmic compensation to the construction of comprehensive, standardized, large datasets. Once high-performance AI models are trained on such well-curated data ecosystems, explainable AI (XAI) techniques, such as Shapley additive explanations (SHAP) or partial dependence plots (PDPs), become indispensable for practical experimental validation. Rather than treating a separation membrane model as a black box, XAI techniques quantitatively assess the relative contribution of individual input features, thereby revealing the critical physicochemical descriptors that govern separation performance, including pore confinement geometry, surface charge characteristics, and functional group affinities. Eventually, this interpretability directly guides and streamlines experimental validation. Instead of trial and error, researchers can selectively synthesize materials by purposefully tuning these AI-identified key factors, thus efficiently optimizing membrane separation performance and closing the loop between data-driven hypotheses and physical reality.
2.3. Challenges in multiscale integration
Advanced membrane separation systems are inherently complex, multiscale engineering systems. Their research and development span a broad spectrum: from microscale molecular configurations and foundational computational chemistry derivations, through mesoscale membrane morphologies, to macroscale membrane module hydrodynamics and system-level energy optimization. Current AI applications, however, are predominantly confined to single-scale domains, such as high-throughput screening strictly at the materials level or the isolated prediction of macroscopic operational parameters. Constructing a cross-scale AI framework capable of seamlessly bridging fundamental molecular dynamics with macroscopic process remains a formidable challenge in both computational architecture and algorithmic design (
Fig. 4).
Notably, such a cross-scale AI framework would not be a single, monolithic model computing all spatiotemporal scales simultaneously. Instead, it would involve an integrated, multi-step pipeline comprising distinct, scale-specific AI models. To make this multiscale integration feasible, the research community can shift toward surrogate modeling strategies. First, high-fidelity microscale simulations are utilized to generate offline datasets. Next, AI surrogate models are trained to extract fundamental molecular constitutive relations. Finally, these computationally inexpensive AI surrogates are embedded into macroscopic computational fluid dynamics (CFD) solvers as dynamic parameter inputs without requiring CFD solvers to compute the underlying atomic interactions. The outputs of these CFD simulations, including module-level permeate flux distributions, transmembrane pressure (TMP) profiles, and component rejection rates, can then be embedded into process-scale optimizers such as multi-stage membrane cascade simulators, enabling system-level objectives such as energy minimization, product purity targets, and techno-economic optimization to be pursued.
However, practically coupling these models introduces the non-trivial challenge of ensuring data consistency across scales. Physical quantities transferred between scale-specific models must be mutually compatible in their underlying thermodynamic definitions. For instance, self-diffusion coefficients derived from equilibrium molecular dynamics characterize the random thermal motion of individual molecules. In contrast, macroscopic continuum-scale transport models require mutual diffusion to capture concentration-gradient-driven mass flux. Mapping these fundamentally distinct coefficients, especially within dense polymer matrices or high-concentration industrial conditions, constitutes a key technical obstacle to physically consistent cross-scale data transmission and remains largely unaddressed in the membrane separation literature.
Additionally, this multiscale integration is severely bottlenecked by computational feasibility. Training, tuning, and deploying an interconnected matrix of AI models necessitates massive, graphics processing unit (GPU)-accelerated computing clusters with high memory capacity and low-latency inter-node communication. These specialized hardware requirements currently exceed the computing resources available to most individual research institutions, posing a significant infrastructural barrier to the realization of a fully integrated cross-scale AI framework.
2.4. Limited generalizability in industrial scenarios
The majority of current AI models are trained on datasets acquired under highly idealized laboratory conditions, such as pure gas or water permeation tests, single or simple binary mixtures, and short-term operations. However, real industrial separation processes are inherently complex, involving multicomponent competitive adsorption, fluctuating feed composition, coupled mass transport and hydrodynamics, non-ideal solution behavior, and dynamically changing chemical environments. This profound distribution shift between the idealized training domain and practical industrial application scenarios renders AI models highly vulnerable to predictive failure or overfitting when encountering real-world complex feed streams or the long-term evolution of membrane fouling and performance (
Fig. 5). For example, Dasgupta et al. [
8] reported that machine learning models trained exclusively on single-gas adsorption data are incapable of predicting adsorption isotherms for binary gas mixtures.
To bridge this gap, several recent studies have attempted to train models directly on complex, long-term industrial datasets. For instance, Wang and Li [
9] trained multilayer perceptron and long short-term memory (LSTM) artificial neural network (ANN) models on 525-day operational data from a large pilot-scale anaerobic membrane bioreactor (MBR) treating real municipal wastewater, achieving coefficient of determination (
R2) values exceeding 0.91 for TMP prediction. Similarly, Kovacs et al. [
10] utilized four years of high-resolution data from a confidential full-scale MBR facility to build random forest (RF), ANN, and LSTM models, achieving exceptional
R2 values of up to 0.996. Ding et al. [
11] developed a convolutional LSTM network for industrial zero-liquid-discharge (ZLD) systems, demonstrating superior predictive accuracy (7 d,
R2 = 0.942) by capturing both the spatial and temporal dynamics of reverse-osmosis membrane fouling under complex wastewater environments.
These case studies demonstrate that AI can effectively capture complex fouling and transport dynamics; however, they represent site-specific triumphs rather than true generalizability. The models involved are deeply tethered to the unique feed water chemistry, membrane properties, and operational protocols of their respective facilities. A highly accurate model trained on years of data from one specific MBR plant will likely experience severe predictive collapse if directly deployed to a different facility with a distinct feed profile. Moreover, the reliance on massive amounts of continuous, proprietary data from single facilities highlights the persistent barrier of data silos. Therefore, the core difficulty in translating AI-driven membrane technologies is not merely fitting an isolated model to a single industrial plant but developing robust, transferable foundation models capable of generalizing across diverse, unforeseen industrial environments with minimal site-specific retraining.
3. Opportunities for AI in separation membranes
From a comprehensive perspective spanning cutting-edge basic research to industrial applications, AI presents crucial historic opportunities in the field of separation membranes, as detailed below.
3.1. Inverse structure design
The transformative impact of AI on membrane science is shifting the research paradigm from passive forward screening (i.e., selecting from a predefined pool of candidates) to on-demand, customized inverse design. With this approach, researchers can directly define the long-sought ultimate performance targets for artificial separation membranes: ultrahigh permeability, precise molecular/ion sieving capability, and the smart gating behavior characteristic of natural biological membrane channels. Algorithms can then reverse-engineer and generate biomimetic membrane structures with precisely tailored channel topologies, replicating the exceptional separation performance of biological systems to push beyond the traditional permeability-selectivity upper bound. The feasibility of this inverse design approach has already been successfully demonstrated in polymer science, for example, where machine learning and optimization algorithms were utilized to accurately predict the molecular structures of novel polymers satisfying predefined target cloud points and to guide the targeted experimental synthesis of these materials [
12]. Additionally, empowered by generative AI technologies such as variational autoencoders (VAEs), diffusion models, large language models (LLMs), and transformer-based autoregressive generative models, alongside graph neural networks (GNNs), algorithms can freely explore a nearly infinite chemical space [
13], [
14], [
15], [
16].
To overcome the inherent challenges of data scarcity, fragmentation, and positive-result bias, these generative models can be seamlessly coupled with high-throughput automated robotic labs. Guided by active learning algorithms, such self-driving robotic platforms can autonomously synthesize and evaluate AI-proposed membrane candidates, generating standardized, high-quality experimental data—including crucial negative or failed results—in a closed-loop manner. This continuous data-generation feedback loop dynamically refines the AI’s predictive accuracy with minimal initial data dependency. By systematically logging unbiased outcomes that human researchers typically omit, these automated platforms fundamentally provide the robust, standardized benchmarks necessary to guarantee the long-term reproducibility and transferability of AI models across the membrane science community. Built upon this foundation, researchers can precisely design novel materials with specific topological structures, exact pore size distributions, and customized functional groups. Whether dealing with polymer backbones that require fine-tuned chemical and physical properties, covalent organic frameworks (COFs) and MOFs characterized by high specific surface areas and extreme structural tunability, or biomimetic membrane channels with biomimetic gating and mass-transport features, AI can decipher the complex underlying property-process-structure relationships. In doing so, it can provide us with the optimal molecular design blueprints needed to break through physical limits.
3.2. Accelerating the optimization of green membrane fabrication processes
For ultrafiltration, microfiltration, and most nanofiltration polymeric membranes, nonsolvent-induced phase separation (NIPS) is the dominant industrial fabrication method. NIPS has long been highly dependent on toxic polar aprotic solvents such as N-methyl-2-pyrrolidone (NMP), N,N-dimethylformamide (DMF), and N,N-dimethylacetamide (DMAc). With increasingly stringent global environmental regulations, the need to identify green solvents has become an imminent industry consensus. However, the microstructure of membranes (e.g., the compactness of pores and interconnectivity of macrovoids) is highly susceptible to the coupled effects of multidimensional operating parameters, including polymer concentration, additive type, coagulation bath temperature, humidity, and solvent exchange rate. Because the Hansen solubility parameters (dispersion, polarity, and hydrogen bonding) of green solvents differ significantly from those of traditional solvents, merely replacing the solvent causes a drastic shift in the thermodynamic and kinetic trajectories of the entire phase-separation process. Relying on traditional empirical trial-and-error methods to re-explore hundreds or thousands of process combinations is not only a massively expensive engineering endeavor but also severely delays the industrialization of next-generation eco-friendly membranes.
Traditional phase-diagram determination relies on extremely tedious and time-consuming titration experiments. By constructing quantitative structure-property relationship models using GNNs and deep learning, AI can directly leverage molecular topologies to accurately compute Flory-Huggins interaction parameters for complex multicomponent (i.e., polymer-green solvent-nonsolvent-additive) systems [
17]. This enables rapid and high-precision calculation of binodal curves and reliable estimation of spinodal boundaries for ternary and quaternary phase diagrams. Researchers can then rigorously assess the thermodynamic feasibility of fabricating membranes using green solvent systems at a fundamental level.
Beyond thermodynamics, the final morphology of the membrane pore structure depends heavily on the mass-transfer and exchange rate between the solvent and nonsolvent. Utilizing AI surrogate models combined with phase-field modeling, researchers can dynamically simulate the evolutionary trajectory of liquid-liquid phase separation within the coagulation bath [
18]. Thus, researchers can computationally visualize how macrovoids grow or micropores solidify, thereby establishing a direct mapping relationship among the green formulation, phase-inversion kinetics, and microscopic morphology.
In industrial production, membrane optimization often involves conflicting objectives (e.g., simultaneously reducing solvent toxicity and cost, while maximizing water flux and ensuring exceptionally high mechanical strength and rejection rates). Leveraging Bayesian optimization or reinforcement learning algorithms, AI can rapidly iterate within a massive process parameter space encompassing dozens of dimensions to locate the optimal Pareto front [
19]. Consequently, researchers can swiftly pinpoint the optimal process formulation that balances environmental benefits with ultimate separation performance. This will enable the green transition in membrane manufacturing to enter an era of precise navigation driven by data and algorithms.
3.3. Intelligent fluid dynamics design for membrane modules
Centimeter-scale flat-sheet membranes often demonstrate excellent performance in the laboratory. However, their performance typically degrades significantly once rolled into spiral-wound modules or encapsulated into hollow-fiber configurations. This degradation is primarily caused by severe fluid maldistribution and concentration polarization within large-scale modules.
Traditional CFD incurs exceptionally high computational costs when optimizing spacer geometries and flow channel designs [
20]. Integrating physics-informed neural networks (PINNs) or AI-based surrogate models allows for the instantaneous prediction of flow fields, concentration profiles, and shear-stress distributions within complex three-dimensional channels [
21]. Furthermore, bridging the critical multiscale integration gap, these macroscopic CFD surrogate models can be directly coupled with intrinsic membrane permeances and mass-transfer coefficients derived from microscopic machine learning potential (MLP)-accelerated molecular dynamics simulations to establish a truly seamless cross-scale synergistic design framework [
22]. Senior engineers can utilize these AI models to conduct high-throughput evaluations of thousands of novel feed-spacer topologies or biomimetic channel designs. This capability significantly mitigates concentration polarization. Consequently, the overall water production flux and fouling resistance of industrial membrane modules can be elevated to an entirely new level without altering the intrinsic membrane material itself.
3.4. Dynamic prediction and proactive management of membrane fouling
Membrane fouling remains the Achilles heel of membrane technology in water treatment and bioseparation applications. Traditional antifouling strategies are predominantly static, such as surface hydrophilic modification, or reactive, such as initiating chemical cleaning only after the TMP increases. These approaches frequently result in high energy consumption, shortened membrane lifespans, and the excessive use of chemical agents.
To overcome the limited generalizability of laboratory-trained models in highly dynamic industrial scenarios, the Industrial Internet of Things (IIoT), combined with deep time-series prediction models such as LSTM and transformer architectures, enables the creation of dynamic digital twins for membrane separation processes [
23]. By continuously ingesting real-world, fluctuating boundary conditions directly from the industrial site, AI can process multidimensional, high-frequency data in real time, including feed-water quality metrics such as pH, organic concentration, and turbidity, alongside operating pressure and cross-flow velocity. This capability allows for the precise prediction of membrane fouling evolution trajectories over the coming hours or even days. In particular, advanced graph-temporal deep learning architectures have recently demonstrated the ability to seamlessly integrate such complex multidimensional spatial-temporal data, achieving highly accurate long-term fouling predictions even in full-scale dual-membrane systems [
24]. Thus, AI can proactively generate optimal strategies for physical backwashing frequencies, chemical cleaning schedules, and pump-speed adjustments. This paradigm shift from reactive remediation to proactive intelligent defense will significantly reduce costs for large-scale membrane facilities.
3.5. Revealing complex mass-transfer mechanisms in confined spaces
Fluid mass transfer in sub-nanometer channels—such as highly ordered MOF pores and graphene oxide interlayer channels—no longer follows classical continuum mechanics as in solution diffusion or pore flow models. The mass-transfer mechanism becomes extremely complex due to nanoconfinement effects, interfacial charge interactions, and the restructuring of intermolecular hydrogen-bond networks [
25], [
26]. Although traditional molecular dynamics simulations provide microscopic motion trajectories, their massive computational costs typically limit them to very short timescales and extremely small spatial scales. Consequently, traditional computational simulations struggle to capture large-scale slow diffusion or rare transmembrane separation events within confined spaces. The deep integration of AI and physical laws is fundamentally breaking this bottleneck.
AI-driven molecular dynamics introduces MLPs, which completely change the rules of microscopic simulation. By allowing deep learning algorithms to learn and fit potential energy surfaces obtained from first-principles calculations, MLPs exponentially reduce computational costs while maintaining high quantum chemical accuracy. Bocus et al. [
27] successfully leveraged MLPs to approach beyond-quantum chemical accuracy for determining the potential energy surface and
operando dynamics of olefins adsorbed within confined zeolite pores. This technique enables the precise simulation of large-scale and long-duration mass-transfer processes. With this high-precision underlying algorithm, researchers can accurately decipher the dehydration dynamics, electronic polarization effects, and dynamic evolution of binding free energy for molecules or ions in sub-nanometer confined channels. This provides unprecedented high-fidelity theoretical support for understanding the selective-transport mechanism within confined channels.
Purely data-driven models often face a black-box dilemma characterized by poor extrapolation ability and a lack of physical interpretability when dealing with macroscopic or mesoscopic membrane separation processes. To address the lack of physical consistency, PINNs’ dual data-and-physics-driven approach demonstrates irreplaceable dominance. PINNs creatively embed classical physical laws governing fluid and mass transfer, such as mass-conservation equations, Fick’s law of diffusion, and the Navier-Stokes equations, directly into the neural network loss function as penalty terms or regularization mechanisms. This approach has recently been proven highly effective in accurately solving complex mass-transfer partial differential equations at the microscale, such as those within biological cellular structures [
28]. This hybrid architecture combines convincing physical prior knowledge with modern AI computing power, enabling the model to converge and output precise solutions that align with physical intuition, even when experimental data are limited or contain background noise.
4. Conclusions
Although significant progress has been made in AI-empowered separation membrane science, its industrial implementation still presents major challenges. A core issue is how to effectively utilize real-world, cross-scale data (e.g., massive material parameters in the literature, operational logs of industrial modules, and time-series data from quality inspections). Unlike structured test data from ideal laboratory environments, real-world datasets derived from industry and experiments are often fragmented, unstructured, and highly heterogeneous. While leveraging AI to mine such data can greatly advance the development of customized membrane processes and intelligent antifouling, it requires the construction of integrated systems equipped with advanced algorithms to manage this data complexity.
Key priorities in this field also include revealing the complex mass-transfer mechanisms within confined spaces, thereby developing highly efficient, AI-empowered platforms for inverse material design. The paramount value of this inverse design lies not merely in pushing the upper bounds of single-material performance but in realizing true process intensification. At the microscale, for example, AI facilitates the precise matching of catalytic active sites with specific pore confinement geometries (e.g., structurally tunable MOFs). This promotes the deep integration of cross-disciplinary applications, such as advanced catalytic membrane reactors that couple chemical conversion and in situ product separation within a single unit, reducing downstream steps and shifting thermodynamic equilibriums. Achieving these goals relies on scalable and physically interpretable AI systems, which must possess the capability to process multimodal datasets and provide physicochemical insights for research, design, and engineering personnel.
From macroscopic and system-level perspectives, AI surrogate models serve to connect intrinsic material properties and process-level simulation. AI-driven optimizers can rapidly explore wide operational spaces to design unconventional hybrid architectures, such as integrated membrane-distillation or multi-stage cascade layouts, which reduce the complexity of process flowsheets and shrink the occupied area of production installations. In addition, the AI-assisted development of environment-responsive smart membranes allows membrane separation units to accommodate variable industrial feedstock compositions, which may remove expensive upstream pre-processing operations. To realize this fully intensified, streamlined, and intelligent chemical engineering paradigm, future work should focus on establishing unified frameworks for multimodal data integration and open-source sharing within the separation membrane field. Simultaneously, safeguarding the data privacy of core corporate processes through secure data-sharing practices is equally indispensable for maintaining trust in industry-academia-research collaborations. Effectively addressing these challenges will enable AI to make revolutionary contributions to separation membrane science, thereby driving innovations in green membrane fabrication, breaking through performance limits, and comprehensively optimizing future industrial separation membrane systems.
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