Machine learning-assisted development of gas separation membranes: A review DOI Creative Commons

An Li,

Jianchun Chu, Shaoxuan Huang

et al.

Carbon Capture Science & Technology, Journal Year: 2025, Volume and Issue: 14, P. 100374 - 100374

Published: Jan. 30, 2025

Language: Английский

Emerging Trends in Machine Learning: A Polymer Perspective DOI Creative Commons
Tyler B. Martin, Debra J. Audus

ACS Polymers Au, Journal Year: 2023, Volume and Issue: 3(3), P. 239 - 258

Published: Jan. 18, 2023

In the last five years, there has been tremendous growth in machine learning and artificial intelligence as applied to polymer science. Here, we highlight unique challenges presented by polymers how field is addressing them. We focus on emerging trends with an emphasis topics that have received less attention review literature. Finally, provide outlook for field, outline important areas science discuss advances from greater material community.

Language: Английский

Citations

85
Polymeric membranes for industrial applications: Recent progress, challenges and perspectives DOI

Sachin Karki,

Gauri Hazarika,

Diksha Yadav

et al.

Desalination, Journal Year: 2023, Volume and Issue: 573, P. 117200 - 117200

Published: Dec. 4, 2023

Language: Английский

Citations

82
Membranes for CO2 capture and separation: Progress in research and development for industrial applications DOI
Zhongde Dai, Liyuan Deng

Separation and Purification Technology, Journal Year: 2023, Volume and Issue: 335, P. 126022 - 126022

Published: Dec. 14, 2023

Language: Английский

Citations

68
Rethinking the pathway to sustainable fire retardants DOI Creative Commons

Jiabing Feng,

Lei Liu, Yan Zhang

et al.

Exploration, Journal Year: 2023, Volume and Issue: 3(4)

Published: July 6, 2023

Flame retardants are currently used in a wide range of industry sectors for saving lives and property by mitigating fire hazards. The growing safety requirements materials boost an escalating demand consumption retardants. This has significantly driven both the scientific community to pursue sustainable retardants, but what makes flame retardant? Here overview recent advances is offered, their renewable raw materials, green synthesis life cycle assessments highlighted. A discussion on key challenges that hinder innovation design principles creating truly yet cost-effective also presented. short work expected help drive development sustainable, expedite creation more safer society.

Language: Английский

Citations

48
Design of functional and sustainable polymers assisted by artificial intelligence DOI
Tran Doan Huan, Rishi Gurnani,

Chiho Kim

et al.

Nature Reviews Materials, Journal Year: 2024, Volume and Issue: 9(12), P. 866 - 886

Published: Aug. 19, 2024

Language: Английский

Citations

26
Expanding the Horizons of Machine Learning in Nanomaterials to Chiral Nanostructures DOI Creative Commons
Vera Kuznetsova, Áine Coogan,

Dmitry Botov

et al.

Advanced Materials, Journal Year: 2024, Volume and Issue: 36(18)

Published: Jan. 19, 2024

Abstract Machine learning holds significant research potential in the field of nanotechnology, enabling nanomaterial structure and property predictions, facilitating materials design discovery, reducing need for time‐consuming labor‐intensive experiments simulations. In contrast to their achiral counterparts, application machine chiral nanomaterials is still its infancy, with a limited number publications date. This despite great advance development new sustainable high values optical activity, circularly polarized luminescence, enantioselectivity, as well analysis structural chirality by electron microscopy. this review, an methods used studying provided, subsequently offering guidance on adapting extending work nanomaterials. An overview within framework synthesis–structure–property–application relationships presented insights how leverage study these highly complex are provided. Some key recent reviewed discussed Finally, review captures achievements, ongoing challenges, prospective outlook very important field.

Language: Английский

Citations

24
Machine Learning in Membrane Design: From Property Prediction to AI-Guided Optimization DOI Creative Commons
Zhonglin Cao, Omid Barati Farimani, Janghoon Ock

et al.

Nano Letters, Journal Year: 2024, Volume and Issue: 24(10), P. 2953 - 2960

Published: March 4, 2024

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 machine learning (ML) areas scientific discovery, researchers started to tackle problem field membrane design using data-driven ML tools. In this Mini Review, we summarize research efforts on three types design, including (1) property prediction ML, (2) gaining physical insight and drawing quantitative relationships between properties explainable artificial intelligence, (3) ML-guided optimization, virtual screening membranes. On top review previous research, discuss challenges associated with applying for potential future directions.

Language: Английский

Citations

17
MOF membranes for gas separations DOI
Yiming Zhang, Hang Yin,

Lingzhi Huang

et al.

Progress in Materials Science, Journal Year: 2025, Volume and Issue: unknown, P. 101432 - 101432

Published: Jan. 1, 2025

Citations

3
Machine Learning Approaches in Polymer Science: Progress and Fundamental for a New Paradigm DOI Creative Commons
Chunhui Xie, Haoke Qiu, Lu Liu

et al.

SmartMat, Journal Year: 2025, Volume and Issue: 6(1)

Published: Jan. 9, 2025

ABSTRACT Machine learning (ML), material genome, and big data approaches are highly overlapped in their strategies, algorithms, models. They can target various definitions, distributions, correlations of concerned physical parameters given polymer systems, have expanding applications as a new paradigm indispensable to conventional ones. Their inherent advantages building quantitative multivariate largely enhanced the capability scientific understanding discoveries, thus facilitating mechanism exploration, prediction, high‐throughput screening, optimization, rational inverse designs. This article summarizes representative progress recent two decades focusing on design, preparation, application, sustainable development materials based exploration key composition–process–structure–property–performance relationship. The integration both data‐driven insights through ML deepen fundamental discover novel is categorically presented. Despite construction application robust models, strategies algorithms deal with variant tasks science still rapid growth. challenges prospects then We believe that innovation will thrive along approaches, from efficient design applications.

Language: Английский

Citations

2
Machine learning prediction on the fractional free volume of polymer membranes DOI Creative Commons
Lei Tao, Jinlong He,

Tom Arbaugh

et al.

Journal of Membrane Science, Journal Year: 2022, Volume and Issue: 665, P. 121131 - 121131

Published: Oct. 27, 2022

Language: Английский

Citations

45