Energy and AI最新文献_第2页

A hybrid Artificial Intelligence method for estimating flicker in power systems 电力系统闪变估计的混合人工智能方法

IF 9.6

Energy and AI Pub Date : 2025-09-10 DOI: 10.1016/j.egyai.2025.100614

Javad Enayati , Pedram Asef , Alexandre Benoit

{"title":"A hybrid Artificial Intelligence method for estimating flicker in power systems","authors":"Javad Enayati , Pedram Asef , Alexandre Benoit","doi":"10.1016/j.egyai.2025.100614","DOIUrl":"10.1016/j.egyai.2025.100614","url":null,"abstract":"<div><div>This paper introduces a novel hybrid method combining H-<span><math><mi>∞</mi></math></span> filtering and an adaptive linear neuron (ADALINE) network for flicker component estimation in power distribution systems. The proposed method leverages the robustness of the H-<span><math><mi>∞</mi></math></span> filter to extract the voltage envelope under uncertain and noisy conditions, followed by the use of ADALINE to accurately identify the relative amplitudes of flicker components (<span><math><mrow><mi>Δ</mi><msub><mrow><mi>V</mi></mrow><mrow><mi>i</mi></mrow></msub><mo>/</mo><msub><mrow><mi>V</mi></mrow><mrow><mi>t</mi></mrow></msub></mrow></math></span>) at standard IEC-defined frequencies embedded in the envelope. This synergy enables efficient time-domain estimation with rapid convergence and noise resilience, addressing key limitations of existing frequency-domain approaches. Unlike conventional techniques, this hybrid model handles complex power disturbances without prior knowledge of noise characteristics or extensive training. To validate the method’s performance, we conduct simulation studies based on IEC Standard 61000-4-15, supported by statistical analysis, Monte Carlo simulations, and real-world data. Results demonstrate superior accuracy, robustness, and reduced computational load compared to Fast Fourier Transform (FFT) and Discrete Wavelet Transform (DWT)-based estimators.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"22 ","pages":"Article 100614"},"PeriodicalIF":9.6,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145045869","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Machine-learning-accelerated screening of multi-element doped CuSb catalysts for enhanced C2+ selectivity in CO2 electroreduction 机器学习加速筛选多元素掺杂CuSb催化剂以增强CO2电还原中C2+的选择性

IF 9.6

Energy and AI Pub Date : 2025-09-10 DOI: 10.1016/j.egyai.2025.100613

Xin Cheng , Hang Wang , Xun Zhu , Yang Wang , Qian Fu

{"title":"Machine-learning-accelerated screening of multi-element doped CuSb catalysts for enhanced C2+ selectivity in CO2 electroreduction","authors":"Xin Cheng , Hang Wang , Xun Zhu , Yang Wang , Qian Fu","doi":"10.1016/j.egyai.2025.100613","DOIUrl":"10.1016/j.egyai.2025.100613","url":null,"abstract":"<div><div>Electrochemical CO<sub>2</sub> reduction (CO<sub>2</sub>RR) to value-added fuels and chemicals offers a promising route toward carbon neutrality. However, developing efficient and selective catalysts for the generation of multi-carbon (C<sub>2+</sub>) products remains a significant challenge. In this work, we propose a combined density functional theory (DFT) and machine learning (ML) approach to systematically screen CuSb-based catalysts with varying surface Sb atomic fractions and <span><span>non-metal dopants</span><svg><path></path></svg></span> (O, N, S, Se, and P) on the Cu<sub>2</sub>Sb(100) surface for CO<sub>2</sub>RR. Approximately 200 representative adsorption configurations were randomly selected for DFT calculations, which were then used to train a predictive ML model. This model enables high-accuracy predictions of the adsorption energies of key intermediates (*CO and *H) for the remaining uncalculated configurations. By integrating the K-means clustering analysis and the optimal adsorption energy selection criteria based on the Sabatier principle, the candidate configuration with the best potential for C<sub>2+</sub> product formation was identified: O-doped CuSb with a surface Sb atomic fraction of 3/12. Mechanistic studies further reveal that O doping significantly strengthens *CO adsorption while suppressing *H adsorption by modulating the electronic structure, thereby lowering the CO<sub>2</sub>RR energy barrier and improving the thermodynamic selectivity toward C<sub>2+</sub> products. This work not only elucidates the synergistic effect of surface Sb atomic fraction and <span><span>non-metal dopants</span><svg><path></path></svg></span> on CO<sub>2</sub>RR activity, but also establishes a scalable ML prediction and screening framework, providing theoretical support and methodological pathways for the design of high-performance CuSb-based catalysts.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"22 ","pages":"Article 100613"},"PeriodicalIF":9.6,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145096104","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep kernel Bayesian optimisation for closed-loop electrode microstructure design with user-defined properties 深核贝叶斯优化闭环电极微结构设计与用户自定义的属性

IF 9.6

Energy and AI Pub Date : 2025-09-10 DOI: 10.1016/j.egyai.2025.100608

Andrea Gayon-Lombardo , Ehecatl A. del Rio-Chanona , Catalina A. Pino-Muñoz , Nigel P. Brandon

{"title":"Deep kernel Bayesian optimisation for closed-loop electrode microstructure design with user-defined properties","authors":"Andrea Gayon-Lombardo , Ehecatl A. del Rio-Chanona , Catalina A. Pino-Muñoz , Nigel P. Brandon","doi":"10.1016/j.egyai.2025.100608","DOIUrl":"10.1016/j.egyai.2025.100608","url":null,"abstract":"<div><div>The generation of multiphase porous electrode microstructures with optimum morphological and transport properties is essential in the design of improved electrochemical energy storage devices, such as lithium-ion batteries. Electrode characteristics directly influence battery performance by acting as the main sites where the electrochemical reactions coupled with transport processes occur. This work presents a generation-optimisation closed-loop algorithm for the design of microstructures with tailored properties. A deep convolutional Generative Adversarial Network is used as a deep kernel and employed to generate synthetic three-phase three-dimensional images of a porous lithium-ion battery cathode material. A Gaussian Process Regression uses the latent space of the generator and serves as a surrogate model to correlate the morphological and transport properties of the synthetic microstructures. This surrogate model is integrated into a deep kernel Bayesian optimisation framework, which optimises cathode properties as a function of the latent space of the generator. A set of objective functions were defined to perform the maximisation of morphological properties (e.g., volume fraction, specific surface area) and transport properties (relative diffusivity). We demonstrate the ability to perform simultaneous maximisation of correlated properties (specific surface area and relative diffusivity), as well as constrained optimisation of these properties. This is the maximisation of morphological or transport properties constrained by constant values of the volume fraction of the phase of interest. Visualising the optimised latent space reveals its correlation with morphological properties, enabling the fast generation of visually realistic microstructures with customised properties.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"22 ","pages":"Article 100608"},"PeriodicalIF":9.6,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145096068","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An uncertainty-aware bi-level multitask SqueezeNet for dynamic security assessment in power systems with focus on critical generator identification under small and imbalanced datasets 基于不确定感知的双级多任务SqueezeNet的电力系统动态安全评估，重点研究小数据和不平衡数据下关键发电机的识别

IF 9.6

Energy and AI Pub Date : 2025-09-09 DOI: 10.1016/j.egyai.2025.100618

Sasan Azad , Mohammad Taghi Ameli , Amir Reza Shafieinejad , Hossein Ameli , Goran Strbac

{"title":"An uncertainty-aware bi-level multitask SqueezeNet for dynamic security assessment in power systems with focus on critical generator identification under small and imbalanced datasets","authors":"Sasan Azad , Mohammad Taghi Ameli , Amir Reza Shafieinejad , Hossein Ameli , Goran Strbac","doi":"10.1016/j.egyai.2025.100618","DOIUrl":"10.1016/j.egyai.2025.100618","url":null,"abstract":"<div><div>Deep learning (DL)-based methods in pre-fault dynamic security assessment (DSA) have provided significant results, contributing to the safe operation of power systems. However, power systems often suffer from insufficient, small, and imbalanced datasets, which significantly impact the performance of DL-based DSA models. Existing DSA frameworks typically operate as two-class black-box models, assessing only overall system security without providing insights into the causes of insecurity or identifying critical generators (CGs), and they fail to quantify prediction uncertainty. These challenges hinder the implementation of current methods in real-world power systems and reduce operators' confidence in them. To address these issues, this paper proposes an uncertainty-aware bi-level multitask learning framework based on transfer learning and SqueezeNet architecture. The framework assesses system security, identifies CGs during instability, and leverages fine-tuning of a pre-trained SqueezeNet model to facilitate training with limited data. Additionally, evidential deep learning is incorporated to quantify classification uncertainty. Without relying on the complex and challenging data augmentation method, this framework uses a simple technique called optimal classification threshold determination to mitigate the negative impact of imbalanced data on model performance. The optimal threshold is determined by maximizing the area under the receiver operating characteristic (ROC) curve. The application of the proposed method to the IEEE 118-bus system shows its strong performance. These results offer crucial technical insights for the implementation of DL-based DSA in real-world power systems.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"22 ","pages":"Article 100618"},"PeriodicalIF":9.6,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145060520","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing energy control stability under extreme conditions by integrating weather forecasts into ARLEM 通过将天气预报整合到ARLEM中，增强极端条件下的能源控制稳定性

IF 9.6

Energy and AI Pub Date : 2025-09-09 DOI: 10.1016/j.egyai.2025.100617

Vahid M. Nik

{"title":"Enhancing energy control stability under extreme conditions by integrating weather forecasts into ARLEM","authors":"Vahid M. Nik","doi":"10.1016/j.egyai.2025.100617","DOIUrl":"10.1016/j.egyai.2025.100617","url":null,"abstract":"<div><div>Increasing the stability of energy management systems is crucial, especially when facing extreme weather events. A common approach to prevent sudden oscillations is the implementation of predictive models; however, this often requires more complex models and greater computational power. In this work, weather forecasts are integrated into an approach based on Adaptive Reinforcement Learning for Energy Management (ARLEM). Since ARLEM is an online model-free reward-based RL method, it does not need any form of (predictive) modelling and weather forecasts serve as an additional source of information about the agent’s environment. The impacts of incorporating weather forecasts into ARLEM are investigated for a typical urban neighborhood in Stockholm during a winter with two cold waves and considering 17 future climate scenarios for the period of 2040–2069. Multiple adaptive policy schemes are assessed considering four forecast horizons of 3, 6, 12, and 24 h. Results show that integrating weather forecasts into decision-making can significantly reduce fluctuations in the control system, leading to more stable energy management. Moreover, it enhances energy efficiency by reducing both average and peak demands, particularly during extreme weather events. Overall, this contributes to improving the climate resilience of energy systems.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"22 ","pages":"Article 100617"},"PeriodicalIF":9.6,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145060519","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

LFTL: Lightweight feature transfer learning with channel-independent LSTM for distributed PV forecasting LFTL：基于信道独立LSTM的轻量级特征迁移学习，用于分布式PV预测

IF 9.6

Energy and AI Pub Date : 2025-09-08 DOI: 10.1016/j.egyai.2025.100616

Yuanjing Zhuo, Huan Long, Zhi Wu, Wei Gu

{"title":"LFTL: Lightweight feature transfer learning with channel-independent LSTM for distributed PV forecasting","authors":"Yuanjing Zhuo, Huan Long, Zhi Wu, Wei Gu","doi":"10.1016/j.egyai.2025.100616","DOIUrl":"10.1016/j.egyai.2025.100616","url":null,"abstract":"<div><div>Distributed photovoltaic (PV) power forecasting in newly installed systems faces challenges due to inherent stochastic volatilities and limited historical data. This paper proposes a lightweight feature transfer learning (LFTL) method that enables rapid and accurate forecasting of new distributed PVs. Firstly, the raw fluctuating PV data are preprocessed through decomposition to separate low- and high-frequency components. These components are then multi-scale segmented to capture diverse temporal characteristics. Following feature compression and LSTM temporal modeling, the informative features from the source domain enable lightweight transfer. For the target domain, a channel-independent encoder is designed to prevent negative interactions between heterogeneous frequencies. The frequency-fused segment-independent decoder equipped with positional embeddings enables local temporal analysis and reduces error accumulation of multi-step forecasts. LFTL trains with a joint training strategy to avoid negative transfer caused by domain disparity. LFTL consistently outperforms state-of-the-art time-series forecast models while maintaining a relatively low computational overhead based on real-world distributed PV data.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"22 ","pages":"Article 100616"},"PeriodicalIF":9.6,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145096105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An enhanced sorting framework for retired batteries based on multi-dimensional features and an integrated clustering approach 基于多维特征和集成聚类方法的改进退役电池分类框架

IF 9.6

Energy and AI Pub Date : 2025-09-03 DOI: 10.1016/j.egyai.2025.100612

Zhuo Liu , Bumin Meng , Rui Pan , Juan Zhou

{"title":"An enhanced sorting framework for retired batteries based on multi-dimensional features and an integrated clustering approach","authors":"Zhuo Liu , Bumin Meng , Rui Pan , Juan Zhou","doi":"10.1016/j.egyai.2025.100612","DOIUrl":"10.1016/j.egyai.2025.100612","url":null,"abstract":"<div><div>Retired batteries for secondary use offer significant economic benefits and environmental value. Accurate sorting of retired batteries with diverse characteristics can further enhance their application efficiency. However, in practical sorting processes, the presence of redundant features, noise interference, and distribution discrepancies in the data severely limits the accuracy of sorting outcomes. To address these challenges, this paper proposes an enhanced retired battery sorting strategy that incorporates feature selection and a clustering algorithm, aiming to optimize the sorting process from the perspective of feature data. To address feature redundancy and high dimensionality issues, this paper proposes an entropy screening method. The Local Outlier Factor algorithm is used to remove anomalous samples. Subsequently, an ensemble clustering approach is developed based on K-means, Density-Based Spatial Clustering of Applications with Noise, Gaussian Mixture Model, and Spectral clustering, to handle diverse data distributions. The proposed method is validated on 100 retired batteries as well as the large-scale dataset. Additionally, its strong sorting capability and engineering applicability are further demonstrated through carefully designed aging-controlled experiments.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"22 ","pages":"Article 100612"},"PeriodicalIF":9.6,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145010678","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

District heating optimization in residential buildings using reinforcement learning with adaptive context-aware predictive environment 基于自适应情景感知预测环境的强化学习的住宅区域供热优化

IF 9.6

Energy and AI Pub Date : 2025-09-03 DOI: 10.1016/j.egyai.2025.100603

Sai Sushanth Varma Kalidindi , Hadi Banaee , Hans Karlsson , Amy Loutfi

{"title":"District heating optimization in residential buildings using reinforcement learning with adaptive context-aware predictive environment","authors":"Sai Sushanth Varma Kalidindi , Hadi Banaee , Hans Karlsson , Amy Loutfi","doi":"10.1016/j.egyai.2025.100603","DOIUrl":"10.1016/j.egyai.2025.100603","url":null,"abstract":"<div><div>As district heating networks evolve to meet climate-neutral objectives, optimizing their control under heterogeneous building characteristics and dynamic environmental conditions remains a significant challenge. Traditional control strategies often lack the adaptability necessary to account for building-specific dynamics and to ensure real-time adherence to operational safety constraints. In this work, we present an integrated machine learning-based framework that combines an adaptive context-aware transformer model with deep reinforcement learning to address these limitations. The proposed approach introduces an adaptive context-aware transformer as a predictive environment within a Deep Q-Network (DQN) framework, enabling data-driven, building-specific control of district heating systems. Utilizing real-world data from 148 residential buildings across Sweden and Finland, the model incorporates contextual embeddings and temporal features to predict indoor temperature trajectories with high accuracy, achieving root mean square error values between 0.18–0.24 °C for Swedish buildings and 0.26–0.32 °C for Finnish buildings. The DQN agent leverages these predictions to optimize heating control while ensuring compliance with operational safety limits and preserving occupant comfort. Experimental results demonstrate significant energy savings, with mid-rise buildings achieving up to 14.85% reduction in energy consumption, and peak seasonal savings exceeding 20% during spring months. This integrated approach illustrates the potential for substantial energy optimization and reliable indoor climate management in future district heating networks.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"22 ","pages":"Article 100603"},"PeriodicalIF":9.6,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145096079","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Prediction of geothermal heat flow for sustainable energy applications with sparse geological data using machine learning 利用机器学习的稀疏地质数据预测可持续能源应用中的地热热流

IF 9.6

Energy and AI Pub Date : 2025-09-03 DOI: 10.1016/j.egyai.2025.100615

Qianqian Li , Shuaibin Wan , Haoming Li , Jishan He , Dongxu Ji

{"title":"Prediction of geothermal heat flow for sustainable energy applications with sparse geological data using machine learning","authors":"Qianqian Li , Shuaibin Wan , Haoming Li , Jishan He , Dongxu Ji","doi":"10.1016/j.egyai.2025.100615","DOIUrl":"10.1016/j.egyai.2025.100615","url":null,"abstract":"<div><div>Geothermal heat flow (GHF) is a crucial metric in the assessment of geothermal reservoirs. To circumvent the high cost of conventional GHF measurement techniques, there is a growing interest in leveraging machine learning models to predict GHF based on geological datasets imputed by the Kriging method. However, the spatial distribution of some geological features exhibits complex data patterns and missing values, justifying the need for a more accurate and efficient alternative to the conventional Kriging method. In this study, we present a novel machine learning-based framework for predicting GHF based on sparse geological data. Specifically, a machine learning model (here MissForest) is employed to impute the missing values of geological data. The MissForest model, by leveraging spatial correlations among geological parameters (e.g., upper crust thickness, Moho depth, and rock type), achieves superior imputation accuracy (R<sup>2</sup> = 0.90 at 20% missing rate) over the conventional Kriging method (R<sup>2</sup> = 0.84). Based on the imputed datasets, machine learning regression models are trained to capture the mapping of geological features to GHF. Our best model achieves a low error of 10.18 % for predicting GHF across various regions, surpassing the previous studies. Furthermore, the machine learning-based framework successfully predicts the GHF globally, shedding new light on the distribution patterns of geothermal resources and their exploitation potential worldwide.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"22 ","pages":"Article 100615"},"PeriodicalIF":9.6,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145010677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Opportunities and perspectives of artificial intelligence in electrocatalysts design for water electrolysis 人工智能在水电解电催化剂设计中的机遇与前景

IF 9.6

Energy and AI Pub Date : 2025-09-02 DOI: 10.1016/j.egyai.2025.100606

Qing Wang , Lizhen Wu , Qiang Zheng , Liang An

{"title":"Opportunities and perspectives of artificial intelligence in electrocatalysts design for water electrolysis","authors":"Qing Wang , Lizhen Wu , Qiang Zheng , Liang An","doi":"10.1016/j.egyai.2025.100606","DOIUrl":"10.1016/j.egyai.2025.100606","url":null,"abstract":"<div><div>As a key pathway for green hydrogen production, water electrolysis is expected to play a central role in the future energy landscape. However, its large-scale deployment is hindered by challenges related to cost, performance, and durability. The emergence of artificial intelligence (AI) has transformed this field by offering powerful and efficient tools for the design and optimization of electrocatalysts. This review outlines an AI-driven multiscale design framework, highlighting its role at the microscopic scale for identifying atomic-level active sites and key descriptors, at the mesoscopic scale for structural and morphological characterization, and at the macroscopic scale for multi-objective optimization and intelligent control. This multiscale framework demonstrates the potential of AI to accelerate the development of next-generation electrocatalysts. In addition, the integration of generative AI and automated experimental techniques is highlighted as promising strategies to further enhance electrocatalyst discovery and promote the practical implementation of water electrolysis technologies.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"22 ","pages":"Article 100606"},"PeriodicalIF":9.6,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145007632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0