Predicting elastic modulus of porous La0.6Sr0.4Co0.2Fe0.8O3-δ cathodes from microstructures via FEM and deep learning

Liu, Xuhao; Yan, Zhimiao; Zhong, Zheng

doi:10.1016/j.ijhydene.2021.04.033

Cited by 23 publications

(6 citation statements)

References 60 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The flow is shown in Figure 13. Liu et al [35] developed a deep learning accelerated homogenization framework for predicting the elastic modulus of porous materials directly from their internal microstructure, and the framework construction process is shown in Figure 14. A CNN was built on the foundation of a large amount of simulation data.…”

Section: Intelligent Optimization Algorithmsmentioning

confidence: 99%

See 1 more Smart Citation

Application of Machine Learning in Fuel Cell Research

Zheng

et al. 2023

Energies

View full text Add to dashboard Cite

A fuel cell is an energy conversion device that utilizes hydrogen energy through an electrochemical reaction. Despite their many advantages, such as high efficiency, zero emissions, and fast startup, fuel cells have not yet been fully commercialized due to deficiencies in service life, cost, and performance. Efficient evaluation methods for performance and service life are critical for the design and optimization of fuel cells. The purpose of this paper was to review the application of common machine learning algorithms in fuel cells. The significance and status of machine learning applications in fuel cells are briefly described. Common machine learning algorithms, such as artificial neural networks, support vector machines, and random forests are introduced, and their applications in fuel cell performance prediction and optimization are comprehensively elaborated. The review revealed that machine learning algorithms can be successfully used for performance prediction, service life prediction, and fault diagnosis in fuel cells, with good accuracy in solving nonlinear problems. Combined with optimization algorithms, machine learning models can further carry out the optimization of design and operating parameters to achieve multiple optimization goals with good accuracy and efficiency. It is expected that this review paper could help the reader comprehend the state of the art of machine learning applications in fuel fuels and shed light on further development directions in fuel cell research.

show abstract

Section: Intelligent Optimization Algorithmsmentioning

confidence: 99%

“…A CNN was built on the foundation of a large amount of simulation data. Using the DL and FEM methods, the elastic modulus of a porous LSCF cathode is predicted based on its internal microstructure [35].…”

Section: Intelligent Optimization Algorithmsmentioning

confidence: 99%

Application of Machine Learning in Fuel Cell Research

Zheng

et al. 2023

Energies

View full text Add to dashboard Cite

show abstract

“…Moreover, data-driven surrogate models for property evaluation, for example, convolutional neural networks (CNNs), often directly take input electrode microstructures without any physical feature engineering and can only predict low-dimensional structural properties such as elastic properties and effective diffusivities. [35][36][37][38] Generally, the physical nature of electrochemical processes involves multiple underlying phenomena that critically determine the electrochemical performance. Therefore, it is vital to feed identified electrochemical knowledge into ML techniques as prior information for electrode candidate generation and performance evaluation to finally achieve rational data-driven electrode microstructure design.…”

Section: Introductionmentioning

confidence: 99%

π Learning: A Performance‐Informed Framework for Microstructural Electrode Design

Niu

Zhao

et al. 2023

Advanced Energy Materials

View full text Add to dashboard Cite

batteries, electrolyzers, and electrochemical CO 2 reduction reactors all depend heavily on the nature of the meso-scale reaction sites and charge conductivity in their electrodes. [2][3][4] The meso-scale electrode structure is often determined by the complex interactions of various phases under different thermal, mechanical, and chemical conditions. Therefore, how to design the next generation of high-performance electrodes remains a critical challenge.Data-driven design based on machine learning (ML) has been increasingly recognized as a revolutionary technology in structure design and has been demonstrated to successfully identify various excellent structures in protein sequences, [5] drug molecules, [6] and nanomaterials. [7] This paradigm opens the door for innovative design of high-performance catalyst materials and electrodes in electrochemical engineering. [8][9][10] A typical example is to accelerate the discovery of optimal atomic structures and compositions driven by density functional theory (DFT) calculations. [11][12][13] For instance, Ma et al. [14] proposed two deep learning algorithms to screen emerging electrode materials for sodium-ion and potassium-ion batteries. This data-driven approach for material screening is promising to accelerate the discovery of high-performance electrode materials. Benayad [15] et al. reviewed recent ML applications in Designing high-performance porous electrodes is the key to next-generation electrochemical energy devices. Current machine-learning-based electrode design strategies are mainly orientated toward physical properties; however, the electrochemical performance is the ultimate design objective. Performance-orientated electrode design is challenging because the current data driven approaches do not accurately extract high-dimensional features in complex multiphase microstructures. Herein, this work reports a novel performance-informed deep learning framework, termed π learning, which enables performance-informed microstructure generation, toward overall performance prediction of candidate electrodes by adding most relevant physical features into the learning process. This is achieved by integrating physicsinformed generative adversarial neural networks (GANs) with convolutional neural networks (CNNs) and with advanced multi-physics, multi-scale modeling of 3D porous electrodes. This work demonstrates the advantages of π learning by employing two popular design philosophies: forward and inverse designs, for the design of solid oxide fuel cells electrodes. π learning thus has the potential to unlock performance-driven learning in the design of next generation porous electrodes for advanced electrochemical energy devices such as fuel cells and batteries.

show abstract

“…A CNN consists of one or more convolutional layers and fully connected layers, also including correlation weights and pooling layers, which enables CNNs to utilize the two-or three-dimensional structure of input data. Therefore, CNN-based deep learning has been successfully used to link images to different properties in many fields such as materials science (Tan et al, 2020;Liu et al, 2021b;Wei et al, 2021). For instance, Xie et al (Xie and Grossman, 2018) developed crystal graph convolutional neural networks (CGCNN) that took a variety of inorganic crystals as input and successfully predicted the absolute energy, band gap, and Fermi energy, etc.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Data augmentation and data mining towards microstructure and property relationship for composites

Guo

Liu

Pan

et al. 2023

Self Cite

View full text Add to dashboard Cite

PurposeIn recent years, the convolutional neural network (CNN) based deep learning approach has succeeded in data-mining the relationship between microstructures and macroscopic properties of materials. However, such CNN models usually rely heavily on a large set of labeled images to ensure the accuracy and generalization ability of the predictive models. Unfortunately, in many fields, acquiring image data is expensive and inconvenient. This study aims to propose a data augmentation technique to enhance the performance of the CNN models for linking microstructural images to the macroscopic properties of composites.Design/methodology/approachMicrostructures of composites are synthesized using discrete element simulations and Potts kinetic Monte Carlo simulations. Macroscopic properties such as the elastic modulus, Poisson's ratio, shear modulus, coefficient of thermal expansion, and triple-phase boundary length density are extracted on representative volume elements. The CNN model is trained using the 3D microstructural images as inputs and corresponding macroscopic properties as the labels. The comparison of the predictive performance of the CNN models with and without data augmentation treatment are compared.FindingsThe comparison between the prediction performance of CNN models with and without data augmentation showed that the former reduced the weighted mean absolute percentage error (WMAPE) for the prediction from 5.1627% to 1.7014%. This significant reduction signifies that the proposed data augmentation method can effectively enhance the generalization ability and robustness of CNN models.Originality/valueThis study demonstrates that data augmentation is beneficial for solving the problems of model overfitting, data scarcity, and sample imbalance for CNN-based deep learning tasks at a low cost. By developing more and advanced data augmentation techniques, deep learning accelerated homogenization will boost the multi-scale computational mechanics and materials.

show abstract

Predicting elastic modulus of porous La0.6Sr0.4Co0.2Fe0.8O3-δ cathodes from microstructures via FEM and deep learning

Cited by 23 publications

References 60 publications

Application of Machine Learning in Fuel Cell Research

Application of Machine Learning in Fuel Cell Research

π Learning: A Performance‐Informed Framework for Microstructural Electrode Design

Data augmentation and data mining towards microstructure and property relationship for composites

Contact Info

Product

Resources

About