Artificial Intelligence‐Enabled Sensing Technologies in the 5G/Internet of Things Era: From Virtual Reality/Augmented Reality to the Digital Twin

Advanced Intelligent Systems, Год журнала: 2022, Номер 4(7)

Опубликована: Март 29, 2022

With the development of 5G and Internet Things (IoT), era big data‐driven product design is booming. In addition, artificial intelligence (AI) also emerging evolving by recent breakthroughs in computing power software architectures. this regard, digital twin, analyzing various sensor data with help AI algorithms, has become a cutting‐edge technology that connects physical virtual worlds, which sensors are highly desirable to collect environmental information. However, although existing technologies, including cameras, microphones, inertial measurement units, etc., widely used as sensing elements for applications, high‐power consumption battery replacement them still problem. Triboelectric nanogenerators (TENGs) self‐powered supply feasible platform realizing self‐sustainable low‐power systems. Herein, progress on TENG‐based intelligent systems, is, wearable electronics, robot‐related smart homes, followed prospective future enabled fusion technology, focused on. Finally, how apply systems IoT discussed.

Язык: Английский

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Harnessing the power of machine learning for carbon capture, utilisation, and storage (CCUS) – a state-of-the-art review

Energy & Environmental Science, Год журнала: 2021, Номер 14(12), С. 6122 - 6157

Опубликована: Янв. 1, 2021

A review of the state-of-the-art applications machine learning for CO 2 capture, transport, storage, and utilisation.

Язык: Английский

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Generative Models as an Emerging Paradigm in the Chemical Sciences

Journal of the American Chemical Society, Год журнала: 2023, Номер 145(16), С. 8736 - 8750

Опубликована: Апрель 13, 2023

Traditional computational approaches to design chemical species are limited by the need compute properties for a vast number of candidates, e.g., discriminative modeling. Therefore, inverse methods aim start from desired property and optimize corresponding structure. From machine learning viewpoint, problem can be addressed through so-called generative Mathematically, models defined probability distribution function given molecular or material In contrast, model seeks exploit joint with target characteristics. The overarching idea modeling is implement system that produces novel compounds expected have set features, effectively sidestepping issues found in forward process. this contribution, we overview critically analyze popular algorithms like adversarial networks, variational autoencoders, flow, diffusion models. We highlight key differences between each models, provide insights into recent success stories, discuss outstanding challenges realizing discovered solutions applications.

Язык: Английский

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Understanding ligand-protected noble metal nanoclusters at work

Nature Reviews Materials, Год журнала: 2023, Номер 8(6), С. 372 - 389

Опубликована: Фев. 20, 2023

Язык: Английский

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Machine learning for advanced energy materials

Energy and AI, Год журнала: 2021, Номер 3, С. 100049 - 100049

Опубликована: Янв. 24, 2021

The screening of advanced materials coupled with the modeling their quantitative structural-activity relationships has recently become one hot and trending topics in energy due to diverse challenges, including low success probabilities, high time consumption, computational cost associated traditional methods developing materials. Following this, new research concepts technologies promote development necessary. latest advancements artificial intelligence machine learning have therefore increased expectation that data-driven science would revolutionize scientific discoveries towards providing paradigms for Furthermore, current advances engineering also demonstrate application technology not only significantly facilitate design but enhance discovery deployment. In this article, importance necessity contributing global carbon neutrality are presented. A comprehensive introduction fundamentals is provided, open-source databases, feature engineering, algorithms, analysis model. Afterwards, progress alkaline ion battery materials, photovoltaic catalytic dioxide capture discussed. Finally, relevant clues successful applications remaining challenges highlighted.

Язык: Английский

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Applying Machine Learning to Rechargeable Batteries: From the Microscale to the Macroscale

Angewandte Chemie International Edition, Год журнала: 2021, Номер 60(46), С. 24354 - 24366

Опубликована: Июль 1, 2021

Abstract Emerging machine learning (ML) methods are widely applied in chemistry and materials science studies have led to a focus on data‐driven research. This Minireview summarizes the application of ML rechargeable batteries, from microscale macroscale. Specifically, offers strategy explore new functionals for density functional theory calculations potentials molecular dynamics simulations, which expected significantly enhance challenging descriptions interfaces amorphous structures. also possesses great potential mine unveil valuable information both experimental theoretical datasets. A quantitative “structure–function” correlation can thus be established, used predict ionic conductivity solids as well battery lifespan. exhibits advantages optimization, such fast‐charge procedures. The future combination multiscale experiments, is discussed role humans research highlighted.

Язык: Английский

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14 examples of how LLMs can transform materials science and chemistry: a reflection on a large language model hackathon

Digital Discovery, Год журнала: 2023, Номер 2(5), С. 1233 - 1250

Опубликована: Янв. 1, 2023

We report the findings of a hackathon focused on exploring diverse applications large language models in molecular and materials science.

Язык: Английский

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Material Evolution with Nanotechnology, Nanoarchitectonics, and Materials Informatics: What will be the Next Paradigm Shift in Nanoporous Materials?

Advanced Materials, Год журнала: 2021, Номер 34(7)

Опубликована: Окт. 13, 2021

Abstract Materials science and chemistry have played a central significant role in advancing society. With the shift toward sustainable living, it is anticipated that development of functional materials will continue to be vital for sustaining life on our planet. In recent decades, rapid progress has been made owing advances experimental, analytical, computational methods, thereby producing several novel useful materials. However, most problems material are highly complex. Here, best strategy via implementation three key concepts discussed: nanotechnology as game changer, nanoarchitectonics an integrator, informatics super‐accelerator. Discussions from conceptual viewpoints example developments, chiefly focused nanoporous materials, presented. It coupling these strategies together open advanced routes swift design exploratory search truly solving real‐world problems. These result evolution

Язык: Английский

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Transfer learning based physics-informed neural networks for solving inverse problems in engineering structures under different loading scenarios

Computer Methods in Applied Mechanics and Engineering, Год журнала: 2022, Номер 405, С. 115852 - 115852

Опубликована: Дек. 28, 2022

Язык: Английский

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Carbon dioxide separation and capture by adsorption: a review

Environmental Chemistry Letters, Год журнала: 2023, Номер 21(4), С. 2041 - 2084

Опубликована: Март 16, 2023

Язык: Английский

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