Emerging Trends in Machine Learning: A Polymer Perspective

ACS Polymers Au, Год журнала: 2023, Номер 3(3), С. 239 - 258

Опубликована: Янв. 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.

Язык: Английский

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

Desalination, Год журнала: 2023, Номер 573, С. 117200 - 117200

Опубликована: Дек. 4, 2023

Язык: Английский

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Membranes for CO2 capture and separation: Progress in research and development for industrial applications

Separation and Purification Technology, Год журнала: 2023, Номер 335, С. 126022 - 126022

Опубликована: Дек. 14, 2023

Язык: Английский

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Rethinking the pathway to sustainable fire retardants

Exploration, Год журнала: 2023, Номер 3(4)

Опубликована: Июль 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.

Язык: Английский

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Design of functional and sustainable polymers assisted by artificial intelligence

Nature Reviews Materials, Год журнала: 2024, Номер 9(12), С. 866 - 886

Опубликована: Авг. 19, 2024

Язык: Английский

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Expanding the Horizons of Machine Learning in Nanomaterials to Chiral Nanostructures

Advanced Materials, Год журнала: 2024, Номер 36(18)

Опубликована: Янв. 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.

Язык: Английский

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Machine Learning in Membrane Design: From Property Prediction to AI-Guided Optimization

Nano Letters, Год журнала: 2024, Номер 24(10), С. 2953 - 2960

Опубликована: Март 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.

Язык: Английский

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Machine Learning Approaches in Polymer Science: Progress and Fundamental for a New Paradigm

SmartMat, Год журнала: 2025, Номер 6(1)

Опубликована: Янв. 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.

Язык: Английский

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MOF membranes for gas separations

Progress in Materials Science, Год журнала: 2025, Номер unknown, С. 101432 - 101432

Опубликована: Янв. 1, 2025

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Machine Learning in Computational Design and Optimization of Disordered Nanoporous Materials

Materials, Год журнала: 2025, Номер 18(3), С. 534 - 534

Опубликована: Янв. 24, 2025

This review analyzes the current practices in data-driven characterization, design and optimization of disordered nanoporous materials with pore sizes ranging from angstroms (active carbon polymer membranes for gas separation) to tens nm (aerogels). While machine learning (ML)-based prediction screening crystalline, ordered porous are conducted frequently, porosity receive much less attention, although ML is expected excel field, which rich ill-posed problems, non-linear correlations a large volume experimental results. For micro- mesoporous solids carbons, silica, aerogels, etc.), obstacles mostly related navigation available data transferrable easily interpreted features. The majority published efforts based on obtained same work, datasets often very small. Even limited data, helps discover non-evident serves material production optimization. development comprehensive databases low-level structural sorption characteristics, as well automated synthesis/characterization protocols, seen direction immediate future. paper written language readable by chemist unfamiliar science specifics.

Язык: Английский

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