2026-10-13T14:26:39Z https://api.figshare.com/v2/oai

oai:figshare.com:article/34028609 2026-09-30T02:08:42Z category_4 category_734 category_931 category_106 portal_63 item_type_6 month_year_09_2026

Learning the Path to Ion Separation: A Proof-of-Concept for Machine Learning-Assisted Inverse Design (MLAID) of Nanofiltration Membranes Nohyeong Jeong (10290883) Elif Demirel (20435616) Changyoon Jun (25139819) Yongsheng Chen (632502) Biochemistry Biological Sciences not elsewhere classified Information Systems not elsewhere classified Science Policy validation become available specific data sets shapley additive explanations derived membrane knowledge producing polyamide membranes experimental decision gate ranked experimental candidates nanofiltration membranes advancing membrane fabrication data fabrication conditions within 2 </ sub assisted inverse design whereas machine learning combine machine learning encoded design space study applies mlaid 4 </ sub machine learning prior inverse polymeric membranes experimental benchmark design space study demonstrates prioritized candidates defined nanofiltration design strategies workflow backbone wide range water permeabilities underlying patterns translating literature traditional trial testing served targeted permeability separation challenges promising monomers prioritize monomer powerful strategy material screening large monomer ion separation guided prioritization extensible workflow discovery offers condition combinations bayesian optimization attribution patterns accelerate identification Advancing the design of polymeric membranes through material screening and discovery offers a powerful strategy to guide future membrane fabrication tailored to a wide range of separation challenges. Traditional trial-and-error methods often fall short in navigating this large monomer and process-design space, whereas machine learning-assisted inverse design (MLAID) can provide an extensible workflow for translating literature-derived membrane knowledge into ranked experimental candidates. Building on prior inverse-design strategies that combine machine learning (ML) with Bayesian optimization (BO), this study applies MLAID to prioritize membrane candidates within a target-defined nanofiltration (NF) design space. MLAID models are trained with membrane fabrication data, and their underlying patterns are interpreted through Shapley additive explanations (SHAP). SHAP was used to investigate model-attribution patterns, and BO was used to prioritize monomer and fabrication-condition combinations for producing polyamide membranes with targeted permeability and selectivity. The MLAID framework prioritized candidate formulations across target salt-water permeabilities of 4 to 12 LMH/bar (L m^–2 h^–1 bar^–1), with Na₂SO₄ rejection used as the experimental benchmark. Fabrication and testing served as the experimental decision gate for evaluating the model-prioritized candidates. Overall, this study demonstrates how ML/BO-guided prioritization can accelerate identification of promising monomers and fabrication conditions within an encoded design space, while providing a workflow backbone that can expand as richer target-specific data sets, descriptors, and validation become available. 2026-09-29T00:00:00Z Text Journal contribution 10.1021/acs.est.5c15241.s001 https://figshare.com/articles/journal_contribution/Learning_the_Path_to_Ion_Separation_A_Proof-of-Concept_for_Machine_Learning-Assisted_Inverse_Design_MLAID_of_Nanofiltration_Membranes/34028609 CC BY-NC 4.0