Machine Learning Accelerated Discovery of Covalent Organic Frameworks for Environmental and Energy Applications

Environmental Science & Technology, Год журнала: 2025, Номер unknown

Опубликована: Март 30, 2025

Covalent organic frameworks (COFs) are porous crystalline materials obtained by linking ligands covalently. Their high surface area and adjustable pore sizes make them ideal for a range of applications, including CO2 capture, CH4 storage, gas separation, catalysis, etc. Traditional methods material research, which mainly rely on manual experimentation, not particularly efficient, while with advancements in computer science, high-throughput computational screening based molecular simulation have become crucial discovery, yet they face limitations terms resources time. Currently, machine learning (ML) has emerged as transformative tool many fields, capable analyzing large data sets, identifying underlying patterns, predicting performance efficiently accurately. This approach, termed "materials genomics", combines ML to predict design high-performance materials, significantly speeding up the discovery process compared traditional methods. review discusses functions screening, design, prediction COFs highlights their applications across various domains like thereby providing new research directions enhancing understanding COF applications.

Язык: Английский

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Carboxyl-functionalized polyimide for polar/non-polar organic solvent separation by pervaporation

Journal of Membrane Science, Год журнала: 2024, Номер 713, С. 123277 - 123277

Опубликована: Сен. 1, 2024

Язык: Английский

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

Environmental Science & Technology, Год журнала: 2024, Номер unknown

Опубликована: Дек. 16, 2024

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

Язык: Английский

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Environmental Machine Learning, Baseline Reporting, and Comprehensive Evaluation: The EMBRACE Checklist

Environmental Science & Technology, Год журнала: 2024, Номер 58(45), С. 19909 - 19912

Опубликована: Окт. 29, 2024

Язык: Английский

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Developing Physiologically Compatible Electron Donors for Reductive Dechlorination by Dissimilatory Iron-Reducing Bacteria Using Machine Learning

ACS ES&T Engineering, Год журнала: 2025, Номер unknown

Опубликована: Фев. 18, 2025

Язык: Английский

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In situ polymerization of UiO-66-NH2 with polyamide to fabricate mixed-matrix membranes with enhanced separation performance for methanol and dimethyl carbonate

Separation and Purification Technology, Год журнала: 2025, Номер unknown, С. 132247 - 132247

Опубликована: Фев. 1, 2025

Язык: Английский

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Computationally complemented insights into new generation solvents for radiation-induced graft polymerization

Materials Today Chemistry, Год журнала: 2025, Номер 45, С. 102610 - 102610

Опубликована: Март 7, 2025

Язык: Английский

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Machine learning in membrane science: Bridging materials, structures, and performance for next-generation membrane design

Separation and Purification Technology, Год журнала: 2025, Номер unknown, С. 133091 - 133091

Опубликована: Апрель 1, 2025

Язык: Английский

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A machine learning feature descriptor approach: Revealing potential adsorption mechanisms for SF6 decomposition product gas-sensitive materials

Journal of Hazardous Materials, Год журнала: 2024, Номер 481, С. 136567 - 136567

Опубликована: Ноя. 19, 2024

Язык: Английский

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High‐Performance and Scalable Organosilicon Membranes for Energy‐Efficient Alcohol Purification

Advanced Functional Materials, Год журнала: 2024, Номер unknown

Опубликована: Дек. 1, 2024

Abstract The production of bio‐alcohol is increasingly gaining international attention due to its potential as a viable alternative fossil fuels and ability mitigate carbon dioxide emissions. However, the cost almost double that fuels, primarily because low yield purification process. Herein, high‐performance scalable organosilicon membrane with high chain flexibility controllable crosslinking density developed for energy‐efficient alcohol purification. synthesized achieves an ultrahigh total flux (5.8 kg·m −2 ·h −1 ) comparable separation factor (8.7) ethanol/water separation, outperforming most state‐of‐the‐art polymer‐based membranes. Integrated experiments molecular dynamics simulations confirm ultrafast permeation originates from flexibility, large fractional free volume, weak interactions between feed molecules universal applicability low‐crosslinking mechanism formation membranes also validated. Moreover, efficiency scalability in production, along stability casting solution, offer promising prospects industrial applications.

Язык: Английский

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