The integral role of high‐entropy alloys in advancing solid‐state hydrogen storage DOI Creative Commons
Zhao Ding, Yuting Li,

Han Jiang

и другие.

Interdisciplinary materials, Год журнала: 2024, Номер unknown

Опубликована: Окт. 16, 2024

Abstract High‐entropy alloys (HEAs) have emerged as a groundbreaking class of materials poised to revolutionize solid‐state hydrogen storage technology. This comprehensive review delves into the intricate interplay between unique compositional and structural attributes HEAs their remarkable performance. By meticulously exploring design strategies synthesis techniques, encompassing experimental procedures, thermodynamic calculations, machine learning approaches, this work illuminates vast potential in surmounting challenges faced by conventional materials. The underscores pivotal role HEAs' diverse elemental landscape phase dynamics tailoring properties. It elucidates complex mechanisms governing absorption, diffusion, desorption within these novel alloys, offering insights enhancing reversibility, cycling stability, safety characteristics. Moreover, it highlights transformative impact advanced characterization techniques computational modeling unraveling structure–property relationships guiding rational high‐performance for applications. bridging gap fundamental science practical implementation, sets stage development next‐generation solutions. identifies key research directions accelerate deployment systems, including optimization routes, integration multiscale characterization, harnessing data‐driven approaches. Ultimately, analysis serves roadmap scientific community, paving way widespread adoption disruptive technology pursuit sustainable efficient clean energy future.

Язык: Английский

A machine learning-based alloy design system to facilitate the rational design of high entropy alloys with enhanced hardness DOI
Yang Chen, Chang Ren, Yuefei Jia

и другие.

Acta Materialia, Год журнала: 2021, Номер 222, С. 117431 - 117431

Опубликована: Ноя. 1, 2021

Язык: Английский

Процитировано

216
Machine learning for high-entropy alloys: Progress, challenges and opportunities DOI Creative Commons
Xianglin Liu, Jiaxin Zhang, Zongrui Pei

и другие.

Progress in Materials Science, Год журнала: 2022, Номер 131, С. 101018 - 101018

Опубликована: Сен. 15, 2022

Язык: Английский

Процитировано

205
Data‐Driven Materials Innovation and Applications DOI
Zhuo Wang, Zhehao Sun, Hang Yin

и другие.

Advanced Materials, Год журнала: 2022, Номер 34(36)

Опубликована: Апрель 22, 2022

Abstract Owing to the rapid developments improve accuracy and efficiency of both experimental computational investigative methodologies, massive amounts data generated have led field materials science into fourth paradigm data‐driven scientific research. This transition requires development authoritative up‐to‐date frameworks for approaches material innovation. A critical discussion on current advances in discovery with a focus frameworks, machine‐learning algorithms, material‐specific databases, descriptors, targeted applications inorganic is presented. Frameworks rationalizing innovation are described, review essential subdisciplines presented, including: i) advanced data‐intensive strategies algorithms; ii) databases related tools platforms generation management; iii) commonly used molecular descriptors processes. Furthermore, an in‐depth broad innovation, such as energy conversion storage, environmental decontamination, flexible electronics, optoelectronics, superconductors, metallic glasses, magnetic materials, provided. Finally, how these (with insights synergy science, tools, mathematics) support paradigms outlined, opportunities challenges highlighted.

Язык: Английский

Процитировано

111
Artificial intelligence-powered electronic skin DOI
Changhao Xu,

Samuel A. Solomon,

Wei Gao

и другие.

Nature Machine Intelligence, Год журнала: 2023, Номер 5(12), С. 1344 - 1355

Опубликована: Дек. 18, 2023

Язык: Английский

Процитировано

107
Disordered enthalpy–entropy descriptor for high-entropy ceramics discovery DOI Creative Commons
Simon Divilov, Hagen Eckert, David Hicks

и другие.

Nature, Год журнала: 2024, Номер 625(7993), С. 66 - 73

Опубликована: Янв. 3, 2024

The need for improved functionalities in extreme environments is fuelling interest high-entropy ceramics

Язык: Английский

Процитировано

102
Machine Learning Interatomic Potentials and Long-Range Physics DOI Creative Commons
Dylan M. Anstine, Olexandr Isayev

The Journal of Physical Chemistry A, Год журнала: 2023, Номер 127(11), С. 2417 - 2431

Опубликована: Фев. 21, 2023

Advances in machine learned interatomic potentials (MLIPs), such as those using neural networks, have resulted short-range models that can infer interaction energies with near ab initio accuracy and orders of magnitude reduced computational cost. For many atom systems, including macromolecules, biomolecules, condensed matter, model become reliant on the description short- long-range physical interactions. The latter terms be difficult to incorporate into an MLIP framework. Recent research has produced numerous considerations for nonlocal electrostatic dispersion interactions, leading a large range applications addressed MLIPs. In light this, we present Perspective focused key methodologies being used where presence physics chemistry are crucial describing system properties. strategies covered include MLIPs augmented corrections, electrostatics calculated charges predicted from atomic environment descriptors, use self-consistency message passing iterations propagated information, obtained via equilibration schemes. We aim provide pointed discussion support development learning-based systems contributions only nearsighted deficient.

Язык: Английский

Процитировано

94
Dual Atom Catalysts for Energy and Environmental Applications DOI
Tiancheng Pu, Jiaqi Ding, Fanxing Zhang

и другие.

Angewandte Chemie International Edition, Год журнала: 2023, Номер 62(40)

Опубликована: Июнь 7, 2023

The pursuit of high metal utilization in heterogeneous catalysis has triggered the burgeoning interest various atomically dispersed catalysts. Our aim this review is to assess key recent findings synthesis, characterization, structure-property relationship and computational studies dual-atom catalysts (DACs), which cover full spectrum applications thermocatalysis, electrocatalysis photocatalysis. In particular, combination qualitative quantitative characterization with cooperation DFT insights, synergies superiorities DACs compare counterparts, high-throughput catalyst exploration screening machine-learning algorithms are highlighted. Undoubtably, it would be wise expect more fascinating developments field as tunable

Язык: Английский

Процитировано

92
Design high-entropy carbide ceramics from machine learning DOI Creative Commons
Jun Zhang, Biao Xu, Yaoxu Xiong

и другие.

npj Computational Materials, Год журнала: 2022, Номер 8(1)

Опубликована: Янв. 14, 2022

Abstract High-entropy ceramics (HECs) have shown great application potential under demanding conditions, such as high stresses and temperatures. However, the immense phase space poses challenges for rational design of new high-performance HECs. In this work, we develop machine-learning (ML) models to discover high-entropy ceramic carbides (HECCs). Built upon attributes HECCs their constituent precursors, our ML demonstrate a prediction accuracy (0.982). Using well-trained models, evaluate single-phase probability 90 that are not experimentally reported so far. Several these predictions validated by experiments. We further establish diagrams non-equiatomic spanning whole composition which regime can be easily identified. Our predict both equiatomic HECs based solely on chemical descriptors transition-metal-carbide paves way high-throughput with superior properties.

Язык: Английский

Процитировано

90
Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects DOI
Yiming Liu, Xinyu Tan, Jie Liang

и другие.

Advanced Functional Materials, Год журнала: 2023, Номер 33(17)

Опубликована: Фев. 15, 2023

Abstract Data‐driven epoch, the development of machine learning (ML) in materials and device design is an irreversible trend. Its ability efficiency to handle nonlinear game‐playing problems unmatched by traditional simulation computing software trial‐error experiments. Perovskite solar cells are complex physicochemical devices (systems) that consist perovskite materials, transport layer electrodes. Predicting properties screening component related strong point ML. However, applications ML has only begun boom last two years, so it necessary provide a review involved technologies, application status, facing urgent challenges blueprint.

Язык: Английский

Процитировано

87
Artificial Intelligence in Physical Sciences: Symbolic Regression Trends and Perspectives DOI Creative Commons
Dimitrios Angelis, Filippos Sofos, Theodoros E. Karakasidis

и другие.

Archives of Computational Methods in Engineering, Год журнала: 2023, Номер 30(6), С. 3845 - 3865

Опубликована: Апрель 19, 2023

Symbolic regression (SR) is a machine learning-based method based on genetic programming principles that integrates techniques and processes from heterogeneous scientific fields capable of providing analytical equations purely data. This remarkable characteristic diminishes the need to incorporate prior knowledge about investigated system. SR can spot profound elucidate ambiguous relations be generalizable, applicable, explainable span over most scientific, technological, economical, social principles. In this review, current state art documented, technical physical characteristics are presented, available investigated, application explored, future perspectives discussed.

Язык: Английский

Процитировано

87