Atomic Defects in Layered Transition Metal Dichalcogenides for Sustainable Energy Storage and the Intelligent Trends in Data Analytics.

Zheng Luo, Ying Yang, Zizhen Fu, Fengqi Liu, Susu Fang, Kele Xu, Shanshan Wang

Abstract readReview

In one paragraph

Review in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

7 authors.

Zheng LuoCollege of Aerospace Science and Engineering, National University of Defense Technology, Changsha, China.

Ying YangCollege of Aerospace Science and Engineering, National University of Defense Technology, Changsha, China.

Zizhen FuCollege of Aerospace Science and Engineering, National University of Defense Technology, Changsha, China.

Fengqi LiuCollege of Aerospace Science and Engineering, National University of Defense Technology, Changsha, China.

Susu FangCollege of Aerospace Science and Engineering, National University of Defense Technology, Changsha, China.

Kele XuSchool of Computer, State Key Laboratory of Complex & Critical Software Environment, National University of Defense Technology, Changsha, China.

Shanshan WangCollege of Aerospace Science and Engineering, National University of Defense Technology, Changsha, China.ORCID https://orcid.org/0000-0003-3750-6737

Funding

Hunan Natural Science Foundation 2025JJ60277Innovation Research Foundation of National University of Defense Technology ZZCX-ZZGC-01-07Major Fundamental Research Project of Hunan Province 2025JC0005National Natural Science Foundation of China 22494643National Natural Science Foundation of China 52172032：52501301National Natural Science Foundation of China 52222201National Postdoctoral Program for Innovative Talents GZC20252799Shenzhen Science and Technology Innovation Commission Project KQTD20221101115627004State Administration of Science, Technology, and Industry for National Defense WDZC20245250509

6 · The paper itself

Abstract

Defects in layered transition metal dichalcogenides play a crucial role in the development of high-energy-density and high-safety electrochemical devices for sustainable energy storage systems. Although transmission electron microscopy prevails as an indispensable tool for visualizing defects at the atomic scale, manual analysis in such data-intensive scenarios generally lacks both efficiency and accuracy. Fortunately, the emergence of machine learning is transforming the paradigm of electron microscopy data analytics, offering a potent tool to expedite the discovery of novel structures and knowledge. In this review, we briefly introduce the atomic structures of typical defect configurations in transition metal dichalcogenides, along with their beneficial effects on electrochemical redox kinetics and stability when used in batteries and supercapacitors. Then, the latest innovations in the defect-engineered transition metal dichalcogenides for advanced energy storage devices, and the progress made in machine learning methodologies for their application in high-throughput electron microscopy analytics are systematically summarized. Finally, this review is concluded with perspectives on the remaining challenges and future opportunities in intelligent defect characterizations and engineering toward the next-generation energy storage systems.

Indexed as

atomic defectsmachine learningsustainable energy storageTEM analyticstransition‐metal dichalcogenides

Identifiers

PMID41556218

DOI10.1002/advs.202521502

PMCPMC13325513

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LicenceCC BY

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.