Design of High-Performance MXene-Based 2D Membranes for Desalination via Machine Learning and Hybrid Optimization Algorithms
Bibliographic record
Abstract
The global scarcity of freshwater resources has intensified the demand for efficient seawater desalination technologies. However, conventional approaches, such as reverse osmosis and nanofiltration, often suffer from high energy consumption and limited membrane performance. MXene, a promising two-dimensional (2D) material, offers unique structural and surface chemical properties that can enhance membrane-based separations. This study presents an integrated inverse design framework that combines machine learning (ML), optimization algorithms, and molecular dynamics (MD) simulations to accelerate the development of high-performance MXene membranes. A dual-target predictive model based on XGBoost was constructed to estimate the water flux and salt rejection, identifying surface charge density and pore area as key governing parameters. A range of intelligent optimization strategies was used to design the MXene membrane with a high desalination performance. Especially, the hybrid optimization methods showed clear advantages over baseline algorithms, delivering faster convergence and obtaining an optimized MXene membrane. MD simulations validated the predicted performance of optimized structures, showing good agreement with the prediction results of the ML model. The optimal configuration of Ti 3 C 2 O 2 ─with a charge coefficient of 1.1 and a pore area of 82.72 Å 2 ─achieved an excellent desalination performance with water permeability and salt rejection. This work demonstrates the power of integrating artificial intelligence and molecular modeling for the rational design of desalination membranes. The proposed framework provides a generalizable and scalable approach for advancing the intelligent development of 2D materials in water treatment applications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".