Machine learning guided discovery of super-hard high entropy ceramics

Jaafreh, Russlan; Kang, Yoo Seong; Kim, Jung-Gu; Hamad, Kotiba

doi:10.1016/j.matlet.2021.130899

Cited by 21 publications

(4 citation statements)

References 55 publications

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“…[29] In this method, a compound with a specific crystal structure can be described based on features that consist of two categories, the crystal and chemical composition features. We have employed those types of features in our previous works, where they have been used in the learning process to build ML models for predicting new compounds with ultralow lattice thermal conductivity, [17] and superhard materials, [15] as well. The reasons for this choice of those features are twofold; first, these features are very promising to describe crystalline materials accurately, where the involvement of this type of features enables the distinction between polymorphous materials that have identical chemical compositions, like graphite and diamond, but different crystal characteristics.…”

Section: Feature Generation and Processingmentioning

confidence: 99%

“…Here, a predictive machine learning (ML) model can be built to predict the mechanical stability of any perovskite material based on its composition and crystal structure. [15,16] For example, in our previous work, [17] we employed the ML techniques to build a model that could predict the lattice thermal conductivity of compounds, said model was built based on the data of thermal conductivity values of 110 compounds at a wide range of temperatures, as calculated by density functional theory (DFT) calculations of those compounds. [17] In the present work, however, this method, in which a previous experience is used for the machine to learn and build the model, will be less useful due to the lack of explicit experimental data available on the mechanical stability of these materials.…”

Section: Introductionmentioning

confidence: 99%

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A Machine Learning‐Assisted Approach to a Rapid and Reliable Screening for Mechanically Stable Perovskite‐Based Materials

Jaafreh

Sharan

Sajjad

et al. 2022

Adv Funct Materials

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The present work is designed to discover new perovskite-based materials, which are expected to show high mechanical stability during their applications, using machine learning (ML) techniques, and based on the Pugh's criterion for distinguishing brittle and ductile behaviors. For this purpose, ML models to predict the moduli of materials, bulk (B) and shear (G), are built using their crystal structure and composition information. The ML process is initiated with the information of 5663 compounds, including composition, crystal structure and moduli, as listed in AFLOW database. Following a procedure of data characteristics, feature generation, feature processing, training, and testing, the ML models are constructed with acceptable accuracy (tenfold cross-validation R 2 score of 0.90 and 0.89 for B and G, respectively). The validation process of the models, which is conducted using the corresponding density functional theory calculations, reveals that these models are reliable to be employed in a largescale screening process. Indeed, the B-and G-based ML models are incorporated in a screening process, and this is also conjugated with other screening criterions, to find out thermodynamically stable and formable perovskite-based materials with improved mechanical performance.

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Section: Feature Generation and Processingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Machine Learning‐Assisted Approach to a Rapid and Reliable Screening for Mechanically Stable Perovskite‐Based Materials

Jaafreh

Sharan

Sajjad

et al. 2022

Adv Funct Materials

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“…Very recently, these methods have received great interest by researchers in the various fields of medicine, science, and engineering. For example, in materials science and engineering, ML was used successfully to accelerate the discovery of new materials with high performance [10][11][12][13][14][15]. In this work, the built ML model intends to give an insight into the stress concentration and ductility relationship of Mg and AZ31 Mg alloy; specifically, the kernel average misorientation (KAM) is the chosen indicator of ductility that will be utilized and investigated.…”

Section: Introductionmentioning

confidence: 99%

Interpretable Machine Learning Analysis of Stress Concentration in Magnesium: An Insight beyond the Black Box of Predictive Modeling

2022

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In the present work, machine learning (ML) was employed to build a model, and through it, the microstructural features (parameters) affecting the stress concentration (SC) during plastic deformation of magnesium (Mg)-based materials are determined. As a descriptor for the SC, the kernel average misorientation (KAM) was used, and starting from the microstructural features of pure Mg and AZ31 Mg alloy, as recorded using electron backscattered diffraction (EBSD), the ML model was trained and constructed using various types of ML algorithms, including Logistic Regression (LR), Decision Trees (DT), Random Forest (RF), Naive Bayes Classifier (NBC), K-Nearest Neighbor (KNN), Multilayer Perceptron (MLP), and Extremely Randomized Trees (ERT). The results show that the accuracy of the ERT-based model was higher compared to other models, and accordingly, the nine most-important features in the ERT-based model, those with a Gini impurity higher than 0.025, were extracted. The feature importance showed that the grain size is the most effective microstructural parameter for controlling the SC in Mg-based materials, and according to the relative Accumulated Local Effects (ALE) plot, calculated to show the relationship between KAM and grain size, it was found that SC occurs with a lower probability in the fine range of grain size. All findings from the ML-based model built in the present work were experimentally confirmed through EBSD observations.

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“…With the development of computational science, artificial intelligence has gradually penetrated into the field of materials and data-driven machine learning has been widely used [ 7 , 8 ]. Back propagation neural network (BPNN), as a kind of artificial neural network, can deal with the regression task of complex nonlinear data by back propagation algorithm, which is low-cost and highly efficient [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Creep Curves Based on Back Propagation Neural Networks for Superalloys

Wang

et al. 2022

Materials

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Creep deformation is one of the main failure forms for superalloys during service and predicting their creep life and curves is important to evaluate their safety. In this paper, we proposed a back propagation neural networks (BPNN) model to predict the creep curves of MarM247LC superalloy under different conditions. It was found that the prediction errors for the creep curves were within ±20% after using six creep curves for training. Compared with the θ projection model, the maximum error was reduced by 30%. In addition, it is validated that this method is applicable to the prediction of creep curves for other superalloys such as DZ125 and CMSX-4, indicating that the model has a wide range of applicability.

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Machine learning guided discovery of super-hard high entropy ceramics

Cited by 21 publications

References 55 publications

A Machine Learning‐Assisted Approach to a Rapid and Reliable Screening for Mechanically Stable Perovskite‐Based Materials

A Machine Learning‐Assisted Approach to a Rapid and Reliable Screening for Mechanically Stable Perovskite‐Based Materials

Interpretable Machine Learning Analysis of Stress Concentration in Magnesium: An Insight beyond the Black Box of Predictive Modeling

Prediction of Creep Curves Based on Back Propagation Neural Networks for Superalloys

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