AI-enabled materials discovery for advanced ceramic electrochemical cells

Bello, Idris Temitope; Taiwo, Ridwan; Esan, Oladapo Christopher; Adegoke, Adesola Habeeb; Ijaola, Ahmed Olanrewaju; Li, Zheng; Zhao, Siyuan; Wang, Chen; Shao, Zongping; Ni, Meng

doi:10.1016/j.egyai.2023.100317

Cited by 7 publications

(1 citation statement)

References 280 publications

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“…These trained neural networks have the capability to predict protein properties from gene sequences, and AlphaFold2 has already predicted 98.5% of the structures of human proteins, a significant advancement compared to the previous coverage of only 17% of amino acid residues in human protein sequences after decades of scientific effort [15]. In addition, DL, renowned for its adeptness in discerning intricate data patterns and autonomously extracting features, has found extensive application in materials science [16,17], new energy [18][19][20], medicine [21][22][23], and engineering [24][25][26]. The success of these applications has spurred the further expansion of the DL approach into diverse domains.…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Review of Methods for Hydrological Forecasting Based on Deep Learning

Zhao,

Wang,

Bai

et al. 2024

Water

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Artificial intelligence has undergone rapid development in the last thirty years and has been widely used in the fields of materials, new energy, medicine, and engineering. Similarly, a growing area of research is the use of deep learning (DL) methods in connection with hydrological time series to better comprehend and expose the changing rules in these time series. Consequently, we provide a review of the latest advancements in employing DL techniques for hydrological forecasting. First, we examine the application of convolutional neural networks (CNNs) and recurrent neural networks (RNNs) in hydrological forecasting, along with a comparison between them. Second, a comparison is made between the basic and enhanced long short-term memory (LSTM) methods for hydrological forecasting, analyzing their improvements, prediction accuracies, and computational costs. Third, the performance of GRUs, along with other models including generative adversarial networks (GANs), residual networks (ResNets), and graph neural networks (GNNs), is estimated for hydrological forecasting. Finally, this paper discusses the benefits and challenges associated with hydrological forecasting using DL techniques, including CNN, RNN, LSTM, GAN, ResNet, and GNN models. Additionally, it outlines the key issues that need to be addressed in the future.

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Section: Introductionmentioning

confidence: 99%

A Comprehensive Review of Methods for Hydrological Forecasting Based on Deep Learning

Zhao,

Wang,

Bai

et al. 2024

Water

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Charging Ahead: The Evolution and Reliability of Nickel‐Zinc Battery Solutions

Bello,

Raza,

Michael

et al. 2024

EcoMat

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Nickel‐Zinc (Ni‐Zn) batteries offer an interesting alternative for the expanding electrochemical energy storage industry due to their high‐power density, low cost, and environmental friendliness. However, significant reliability challenges such as capacity fading, self‐discharge, thermal instability, and electrode degradation detract from their competitiveness in the market, hindering their widespread adoption. This study thoroughly examines the degradation mechanisms and approaches to improve the reliability of Ni‐Zn batteries: Starting with their basic chemistry, operating principles, and degradation pathways, strategies for improvement are explored including material modification, electrolyte optimization, cell design approaches, and thermal management techniques. Advanced characterization methods for data collection and reliability assessment are discussed, including electrochemical, structural, spectroscopic, and in situ techniques which are noted for their ability to identify key areas of concern for this cell chemistry. We further consider emerging trends such as novel materials, hybridization with other energy technologies, and the challenges of large‐scale implementation, emphasizing the need for standardized reliability testing protocols. Opportunities for the integration of advanced sensing, such as fiber Bragg grating (FBG) sensors for real‐time monitoring and anomaly detection, along with machine learning (ML) and prognostics and health management of Ni‐Zn batteries are highlighted, as these open the door to future research directions. This comprehensive review should serve as a resource for researchers, engineers, and industry experts aiming to advance and commercialize dependable, high‐performing Ni‐Zn battery technology for a sustainable energy future. image

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