From prediction to design: Recent advances in machine learning for the study of 2D materials

Nano Energy, Год журнала: 2023, Номер 118, С. 108965 - 108965

Опубликована: Окт. 4, 2023

Язык: Английский

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AI for Nanomaterials Development in Clean Energy and Carbon Capture, Utilization and Storage (CCUS)

ACS Nano, Год журнала: 2023, Номер 17(11), С. 9763 - 9792

Опубликована: Июнь 2, 2023

Zero-carbon energy and negative emission technologies are crucial for achieving a carbon neutral future, nanomaterials have played critical roles in advancing such technologies. More recently, due to the explosive growth data, adoption exploitation of artificial intelligence (AI) as part materials research framework had tremendous impact on development nanomaterials. AI has enabled revolutionary next-generation paradigms significantly accelerate all stages material discovery facilitate exploration enormous design space. In this review, we summarize recent advancements applications discovery, with special emphasis selected nanotechnology net-zero future including solar cells, hydrogen energy, battery renewable CO2 capture conversion capture, utilization storage (CCUS) addition, discuss limitations challenges current area by identifying gaps that exist development. Finally, present prospect directions order large-scale

Язык: Английский

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When Machine Learning Meets 2D Materials: A Review

Advanced Science, Год журнала: 2024, Номер 11(13)

Опубликована: Янв. 26, 2024

Abstract The availability of an ever‐expanding portfolio 2D materials with rich internal degrees freedom (spin, excitonic, valley, sublattice, and layer pseudospin) together the unique ability to tailor heterostructures made by in a precisely chosen stacking sequence relative crystallographic alignments, offers unprecedented platform for realizing design. However, breadth multi‐dimensional parameter space massive data sets involved is emblematic complex, resource‐intensive experimentation, which not only challenges current state art but also renders exhaustive sampling untenable. To this end, machine learning, very powerful data‐driven approach subset artificial intelligence, potential game‐changer, enabling cheaper – yet more efficient alternative traditional computational strategies. It new paradigm autonomous experimentation accelerated discovery machine‐assisted design functional heterostructures. Here, study reviews recent progress such endeavors, highlight various emerging opportunities frontier research area.

Язык: Английский

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Recent Advances in Machine Learning‐Assisted Multiscale Design of Energy Materials

Advanced Energy Materials, Год журнала: 2024, Номер unknown

Опубликована: Дек. 10, 2024

Abstract This review highlights recent advances in machine learning (ML)‐assisted design of energy materials. Initially, ML algorithms were successfully applied to screen materials databases by establishing complex relationships between atomic structures and their resulting properties, thus accelerating the identification candidates with desirable properties. Recently, development highly accurate interatomic potentials generative models has not only improved robust prediction physical but also significantly accelerated discovery In past couple years, methods have enabled high‐precision first‐principles predictions electronic optical properties for large systems, providing unprecedented opportunities science. Furthermore, ML‐assisted microstructure reconstruction physics‐informed solutions partial differential equations facilitated understanding microstructure–property relationships. Most recently, seamless integration various platforms led emergence autonomous laboratories that combine quantum mechanical calculations, language models, experimental validations, fundamentally transforming traditional approach novel synthesis. While highlighting aforementioned advances, existing challenges are discussed. Ultimately, is expected fully integrate atomic‐scale simulations, reverse engineering, process optimization, device fabrication, empowering system design. will drive transformative innovations conversion, storage, harvesting technologies.

Язык: Английский

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Data-Driven Discovery of Intrinsic Direct-Gap 2D Materials as Potential Photocatalysts for Efficient Water Splitting

ACS Catalysis, Год журнала: 2024, Номер 14(3), С. 1336 - 1350

Опубликована: Янв. 11, 2024

Intrinsic direct-gap two-dimensional (2D) materials hold great promise as photocatalysts, advancing the application of photocatalytic water splitting for hydrogen production. However, time- and resource-efficient exploration identification such 2D from a vast compositional structural chemical space present significant challenges within realm science research. To this end, we perform data-driven study to find with intrinsic desirable properties overall splitting. By implementing three-staged large-scale screening, which incorporates machine-learned data V2DB, high-throughput density functional theory (DFT), hybrid-DFT calculations, identify 16 promising photocatalysts. Subsequently, conduct comprehensive assessment that are related solar performance, include electronic optical properties, solar-to-hydrogen conversion efficiencies, carrier mobilities. Therefore, not only presents photocatalysts but also introduces rigorous approach future discovery currently unexplored spaces.

Язык: Английский

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Machine learning integrated photocatalysis: progress and challenges

Chemical Communications, Год журнала: 2023, Номер 59(39), С. 5795 - 5806

Опубликована: Янв. 1, 2023

By integrating machine learning with automation and robots, accelerated discovery of photocatalysts in the future could be envisioned.

Язык: Английский

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Solar‐Driven Hydrogen Evolution from Value‐Added Waste Treatment

Advanced Energy Materials, Год журнала: 2024, Номер 14(15)

Опубликована: Фев. 13, 2024

Abstract Hydrogen is one of the most important energy alternatives to conventional fossil‐based fuel. Solar based photocatalytic hydrogen evolution (PHE) a salient approach produce fuel but its efficiency generally limited by sluggish and energy‐unfavorable oxidation reaction. Meanwhile, waste treatment has become worldwide problem clean highly demanded avoid vast greenhouse emission currently. Inspiringly, PHE can be effectively coupled with favorable photooxidation many wastes, which kills two birds stone. In this review, recent progress in presented, where typical solid, liquid, gas wastes have been briefly discussed. Focusing on understanding complicated reaction mechanism revelation products, cutting‐edge techniques for photophysics surface chemistry characterization analyzed, are imperative facilitate following investigation. Finally, developing trend existing issues current research also discussed detail so that holistic blueprint portrayed accelerate their application realistic world.

Язык: Английский

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Recent progress in copper-based inorganic nanostructure photocatalysts: properties, synthesis and photocatalysis applications

Materials Today Sustainability, Год журнала: 2022, Номер 21, С. 100276 - 100276

Опубликована: Ноя. 25, 2022

Язык: Английский

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First-Principles Computational Screening of Two-Dimensional Polar Materials for Photocatalytic Water Splitting

ACS Nano, Год журнала: 2024, Номер 18(29), С. 19381 - 19390

Опубликована: Июль 12, 2024

The band gap constraint of the photocatalyst for overall water splitting limits utilization solar energy. A strategy to broaden range light absorption is employing a two-dimensional (2D) polar material as photocatalyst, benefiting from deflection energy level due their intrinsic internal electric field. Here, by using first-principles computational screening, we search 2D semiconductors photocatalytic both ground- and excited-state perspectives. Applying unique electronic structure model materials, there are 13 candidates hydrogen evolution reaction (HER) 8 oxygen (OER) without barrier energies perspective ground-state free variation calculation. In particular, Cu

Язык: Английский

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Resilient 3D printed porous biodegradable Polylactic Acid coated with Bismuth Ferrite for Piezo Enhanced Photocatalysis degradation assisted by Machine Learning

Nano Energy, Год журнала: 2025, Номер unknown, С. 111010 - 111010

Опубликована: Апрель 1, 2025

Язык: Английский

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