Machine Learning Orchestrating the Materials Discovery and Performance Optimization of Redox Flow Battery DOI Creative Commons
Lina Tang, Puiki Leung, Qian Xu

и другие.

ChemElectroChem, Год журнала: 2024, Номер unknown

Опубликована: Июнь 10, 2024

Abstract This review exploits the crucial role of computational methods in discovering and optimizing materials for redox flow batteries (RFBs). Integration high‐throughput screening (HTCS) machine learning (ML) accelerates discovery, guided by algorithms categorizing RFBs. A collaborative exploration, spanning macroscopic to mesoscopic scales, combines quantum with reinforcement learning, transfer time series analysis, Bayesian optimization, active various generative models. The integration ML techniques experimental methods, anchored experimentally validated Density Functional Theory (DFT) calculations molecular dynamics (MD) simulations, proves indispensable cost‐effective Data collection feature engineering are explored, emphasizing optimization goals precise data within framework. Feature analysis importance is highlighted, utilizing such as filter, embedded, wrapper deep efficient energy exploration. Computational perspectives on features operating conditions encompass membrane characteristics, fluid dynamics, temperature dependence pressure sensitivity. Time‐dependent ML‐generated insights understanding cycling performance intricacies, providing a comprehensive RFB materials.

Язык: Английский

Double transition-metal MXenes: Classification, properties, machine learning, artificial intelligence, and energy storage applications DOI
Iftikhar Hussain, Uzair Sajjad,

Onkar Jaywant Kewate

и другие.

Materials Today Physics, Год журнала: 2024, Номер 42, С. 101382 - 101382

Опубликована: Фев. 25, 2024

Язык: Английский

Процитировано

37
Recent advances in artificial intelligence boosting materials design for electrochemical energy storage DOI Creative Commons
X.-B. Liu, Kexin Fan, Xinmeng Huang

и другие.

Chemical Engineering Journal, Год журнала: 2024, Номер 490, С. 151625 - 151625

Опубликована: Апрель 24, 2024

In the rapidly evolving landscape of electrochemical energy storage (EES), advent artificial intelligence (AI) has emerged as a keystone for innovation in material design, propelling forward design and discovery batteries, fuel cells, supercapacitors, many other functional materials. This review paper elucidates burgeoning role AI materials from foundational machine learning (ML) techniques to its current pivotal advancing frontiers science storage, including enhancing performance, durability, safety battery technologies, cell efficiency longevity, fine-tuning supercapacitors achieve superior capabilities. Collectively, we present comprehensive overview recent advancements that have significantly accelerated development next-generation EES, offering insights into future research trajectories potential unlock new horizons science.

Язык: Английский

Процитировано

34
Source and performance of waste-derived porous carbon material as supercapacitor: Biomass, sludge and plastic waste as precursors DOI
Jinxi Feng, Qi Zhu,

Qingguo Le

и другие.

Renewable and Sustainable Energy Reviews, Год журнала: 2025, Номер 211, С. 115178 - 115178

Опубликована: Янв. 7, 2025

Язык: Английский

Процитировано

8
Machine Learning Model of Polypyrrole Based-Supercapacitor Electrode: Fabrication, Characterization, and Prediction DOI
A. Hefnawy, Jehan El Nady, Mohd Ali Hassan

и другие.

Journal of Alloys and Compounds, Год журнала: 2025, Номер unknown, С. 179240 - 179240

Опубликована: Фев. 1, 2025

Язык: Английский

Процитировано

3
Machine Learning Relationships Between Nanoporous Structures and Electrochemical Performance in MOF Supercapacitors DOI
Zhenxiang Wang, Taizheng Wu, Liang Zeng

и другие.

Advanced Materials, Год журнала: 2025, Номер unknown

Опубликована: Март 6, 2025

The development of supercapacitors is impeded by the unclear relationships between nanoporous electrode structures and electrochemical performance, primarily due to challenges in decoupling complex interdependencies various structural descriptors. While machine learning (ML) techniques offer a promising solution, their application hindered lack large, unified databases. Herein, constant-potential molecular simulation used construct supercapacitor database with hundreds metal-organic framework (MOF) electrodes. Leveraging this database, well-trained decision-tree-based ML models achieve fast, accurate, interpretable predictions capacitance charging rate, experimentally validated representative case. SHAP analyses reveal that specific surface area (SSA) governs gravimetric while pore size effects are minimal, attributed strong dependence electrode-ion coordination on SSA rather than size. porosity, respectively, dominate volumetric 1D-pore 3D-pore MOFs, pinnacling indispensable dimensionality. Meanwhile, porosity found be most decisive factor rate for both MOFs. Especially an exponential increase observed ionic conductance in-pore ion diffusion coefficient, ascribed loosened packing. These findings provide profound insights design high-performance

Язык: Английский

Процитировано

3
Advancement in Supercapacitors for IoT Applications by Using Machine Learning: Current Trends and Future Technology DOI Open Access
Qadeer Akbar Sial, Usman Safder, Shahid Iqbal

и другие.

Sustainability, Год журнала: 2024, Номер 16(4), С. 1516 - 1516

Опубликована: Фев. 10, 2024

Supercapacitors (SCs) are gaining attention for Internet of Things (IoT) devices because their impressive characteristics, including high power and energy density, extended lifespan, significant cycling stability, quick charge–discharge cycles. Hence, it is essential to make precise predictions about the capacitance lifespan supercapacitors choose appropriate materials develop plans replacement. Carbon-based supercapacitor electrodes crucial advancement contemporary technology, serving as a key component among numerous types electrode materials. Moreover, accurately forecasting storage may greatly improve efficient handling system malfunctions. Researchers worldwide have increasingly shown interest in using machine learning (ML) approaches predicting performance The driven by its noteworthy benefits, such improved accuracy predictions, time efficiency, cost-effectiveness. This paper reviews different charge processes, categorizes SCs, investigates frequently employed carbon components. supercapacitors, which applications, affected number capacity, cycle longevity. Additionally, we provide an in-depth review several recently developed ML-driven models used substance properties optimizing effectiveness. purpose these proposed ML algorithms validate anticipated accuracies, aid selection models, highlight future research topics field scientific computing. Overall, this highlights possibility techniques advancements energy-storing device development.

Язык: Английский

Процитировано

16
Utilizing machine learning and deep learning for enhanced supercapacitor performance prediction DOI

Ahmed Emad-Eldeen,

Mohamed Mostafa A. Azim, Montaser Abdelsattar

и другие.

Journal of Energy Storage, Год журнала: 2024, Номер 100, С. 113556 - 113556

Опубликована: Сен. 6, 2024

Язык: Английский

Процитировано

10
Data-based modeling for prediction of supercapacitor capacity: Integrated machine learning and metaheuristic algorithms DOI
Hamed Azimi, Ebrahim Ghorbani‐Kalhor, Seyed Reza Nabavi

и другие.

Journal of the Taiwan Institute of Chemical Engineers, Год журнала: 2025, Номер 170, С. 105996 - 105996

Опубликована: Янв. 31, 2025

Язык: Английский

Процитировано

2
A critical review on pure and hybrid electrode supercapacitors, economics of HESCs, and future perspectives DOI
Khursheed B. Ansari,

Rushda Mashkoor,

Linda Manzanilla

и другие.

Journal of Energy Storage, Год журнала: 2025, Номер 112, С. 115564 - 115564

Опубликована: Янв. 28, 2025

Язык: Английский

Процитировано

1
Evaluating machine learning models comprehensively for predicting maximum power from photovoltaic systems DOI Creative Commons
Samir A. Hamad,

Mohamed A. Ghalib,

Amr Munshi

и другие.

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Март 28, 2025

This paper presents a machine learning (ML) model designed to track the maximum power point of standalone Photovoltaic (PV) systems. Due nonlinear nature generation in PV systems, influenced by fluctuating weather conditions, managing this data effectively remains challenge. As result, use ML techniques optimize systems at their MPP is highly beneficial. To achieve this, research explores various algorithms, such as Linear Regression (LR), Ridge (RR), Lasso (Lasso R), Bayesian (BR), Decision Tree (DTR), Gradient Boosting (GBR), and Artificial Neural Networks (ANN), predict The utilizes from unit's technical specifications, allowing algorithms forecast power, current, voltage based on given irradiance temperature inputs. Predicted also used determine boost converter's duty cycle. simulation was conducted 100 kW solar panel with an open-circuit 64.2 V short-circuit current 5.96 A. Model performance evaluated using metrics Root Mean Square Error (RMSE), Coefficient Determination (R2), Absolute (MAE). Additionally, study assessed correlation feature importance evaluate compatibility factors impacting predictive accuracy models. Results showed that DTR algorithm outperformed others like LR, RR, R, BR, GBR, ANN predicting (Im), (Vm), (Pm) system. achieved RMSE, MAE, R2 values 0.006, 0.004, 0.99999 for Im, 0.015, 0.0036, Vm, 2.36, 0.871, Pm. Factors size training dataset, operating conditions system, type, preprocessing were found significantly influence prediction accuracy.

Язык: Английский

Процитировано

1