Synthesis of high-entropy perovskite metal fluoride anode materials for lithium-ion batteries via a one-pot solution method DOI
Minghao Su, Song Zhu,

Ruijie Yu

et al.

Journal of Alloys and Compounds, Journal Year: 2024, Volume and Issue: 1010, P. 177458 - 177458

Published: Nov. 7, 2024

Language: Английский

Recent Advances in Machine Learning‐Assisted Multiscale Design of Energy Materials DOI Creative Commons
Bohayra Mortazavi

Advanced Energy Materials, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 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.

Language: Английский

Citations

17
Influence of manganese doping on the efficacy of SrCeO3 perovskite utilized as electrodes in supercapacitors DOI
Shaimaa A. M. Abdelmohsen, Haifa A. Alyousef,

Areej Saleh Alqarny

et al.

Journal of the Indian Chemical Society, Journal Year: 2025, Volume and Issue: 102(3), P. 101582 - 101582

Published: Jan. 18, 2025

Language: Английский

Citations

4
Computational understanding and multiscale simulation of secondary batteries DOI
Yan Yuan,

Bin Wang,

Jinhao Zhang

et al.

Energy storage materials, Journal Year: 2025, Volume and Issue: unknown, P. 104009 - 104009

Published: Jan. 1, 2025

Language: Английский

Citations

2
Elucidating the role of multi-scale microstructures in Li7La3Zr2O12 based all-solid-state lithium batteries DOI
Runsheng Yu, Yongjin Chen, Xiang Gao

et al.

Energy storage materials, Journal Year: 2024, Volume and Issue: 72, P. 103752 - 103752

Published: Aug. 30, 2024

Language: Английский

Citations

9
Advancement of capacitive deionization propelled by machine learning approach DOI
Hao Wang, Yuquan Li, Yong Liu

et al.

Separation and Purification Technology, Journal Year: 2024, Volume and Issue: 354, P. 129423 - 129423

Published: Aug. 30, 2024

Language: Английский

Citations

6
Prospects and Challenges of Energy Storage Materials: A Comprehensive Review DOI Creative Commons
Md Mir Shakib Ahmed, Md. Jahid Hasan,

Md. Shakil Chowdhury

et al.

Chemical Engineering Journal Advances, Journal Year: 2024, Volume and Issue: 20, P. 100657 - 100657

Published: Oct. 10, 2024

Language: Английский

Citations

5
SOLID-STATE LITHIUM-ION BATTERY ELECTROLYTES: REVOLUTIONIZING ENERGY DENSITY AND SAFETY DOI Creative Commons

P.U. Nzereogu,

A. Oyesanya,

S.N. Ogba

et al.

Hybrid Advances, Journal Year: 2024, Volume and Issue: unknown, P. 100339 - 100339

Published: Nov. 1, 2024

Language: Английский

Citations

5
Applications of machine learning in surfaces and interfaces DOI Open Access
Shaofeng Xu, Jing‐Yuan Wu, Ying Guo

et al.

Chemical Physics Reviews, Journal Year: 2025, Volume and Issue: 6(1)

Published: March 1, 2025

Surfaces and interfaces play key roles in chemical material science. Understanding physical processes at complex surfaces is a challenging task. Machine learning provides powerful tool to help analyze accelerate simulations. This comprehensive review affords an overview of the applications machine study systems materials. We categorize into following broad categories: solid–solid interface, solid–liquid liquid–liquid surface solid, liquid, three-phase interfaces. High-throughput screening, combined first-principles calculations, force field accelerated molecular dynamics simulations are used rational design such as all-solid-state batteries, solar cells, heterogeneous catalysis. detailed information on for

Language: Английский

Citations

0
Toward unlocking the potential of aqueous Zn-CO2 batteries: What factors affect the electrochemical performance? DOI
Hongyang Zhao, Yue Li, Wang Jian-hu

et al.

Chemical Engineering Journal, Journal Year: 2025, Volume and Issue: unknown, P. 161736 - 161736

Published: March 1, 2025

Language: Английский

Citations

0
Data-driven discovery of vanadium-based anode materials for lithium-ion batteries DOI
Yudi Mo, Zhigang Tang,

Long Zheng

et al.

Journal of Energy Storage, Journal Year: 2025, Volume and Issue: 118, P. 116290 - 116290

Published: March 20, 2025

Language: Английский

Citations

0