Machine learning reveals the importance of the formation enthalpy and atom-size difference in forming phases of high entropy alloys

Zhang, Lei; Chen, Hongmei; Tao, Xiaoma; Cai, Hongguo; Liu, Jingneng; Ouyang, Yifang; Peng, Qing; Du, Youwei

doi:10.1016/j.matdes.2020.108835

Cited by 88 publications

(38 citation statements)

References 68 publications

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“…The purpose of feature selection is to simplify the feature set as much as possible and reduce the adverse effects caused by noise and redundant features while maintaining the description ability of feature set. This improves the accuracy, interpretability and operational efficiency of the model (Zhang et al, 2020).…”

Section: Features Selectionmentioning

confidence: 95%

Pitting Judgment Model Based on Machine Learning and Feature Optimization Methods

Tang²,

Wang

et al. 2021

Front. Mater.

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Pitting corrosion seriously harms the service life of oil field gathering and transportation pipelines, which is an important subject of corrosion prevention. In this study, we collected the corrosion data of pipeline steel immersion experiment and established a pitting judgment model based on machine learning algorithm. Feature reduction methods, including feature importance calculation and pearson correlation analysis, were first adopted to find the important factors affecting pitting. Then, the best input feature set for pitting judgment was constructed by combining feature combination and feature creation. Through receiver operating characteristic (ROC) curve and area under curve (AUC) calculation, random forest algorithm was selected as the modeling algorithm. As a result, the pitting judgment model based on machine learning and high dimensional feature parameters (i.e., material factors, solution factors, environment factors) showed good prediction accuracy. This study provided an effective means for processing high-dimensional and complex corrosion data, and proved the feasibility of machine learning in solving material corrosion problems.

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Section: Features Selectionmentioning

confidence: 95%

Pitting Judgment Model Based on Machine Learning and Feature Optimization Methods

Tang²,

Wang

et al. 2021

Front. Mater.

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“…This work has an advantage of saving costs for generating training set data. The existing work based on raw features and neural networks used at least 80% data of HEA in the training process to classify the phases of HEA 23 , 55 , 56 . The HEA data for training is limited because it is based on the experimental results, and it is hard to get calculation data for its substantial computational cost.…”

Section: Resultsmentioning

confidence: 99%

Accelerated crystal structure prediction of multi-elements random alloy using expandable features

Jin

Park

et al. 2021

Sci Rep

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Properties of solid-state materials depend on their crystal structures. In solid solution high entropy alloy (HEA), its mechanical properties such as strength and ductility depend on its phase. Therefore, the crystal structure prediction should be preceded to find new functional materials. Recently, the machine learning-based approach has been successfully applied to the prediction of structural phases. However, since about 80% of the data set is used as a training set in machine learning, it is well known that it requires vast cost for preparing a dataset of multi-element alloy as training. In this work, we develop an efficient approach to predicting the multi-element alloys' structural phases without preparing a large scale of the training dataset. We demonstrate that our method trained from binary alloy dataset can be applied to the multi-element alloys' crystal structure prediction by designing a transformation module from raw features to expandable form. Surprisingly, without involving the multi-element alloys in the training process, we obtain an accuracy, 80.56% for the phase of the multi-element alloy and 84.20% accuracy for the phase of HEA. It is comparable with the previous machine learning results. Besides, our approach saves at least three orders of magnitude computational cost for HEA by employing expandable features. We suggest that this accelerated approach can be applied to predicting various structural properties of multi-elements alloys that do not exist in the current structural database.

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“…; and alternative approaches such as CALPHAD, ab initio, density functional theory (DFT) and so on; albeit, the accuracy of prediction becomes important [42,43]. The high entropy alloy phase formation is not entirely dependent on any fixed or single approach or rules [44][45][46][47][48]. The phase formation is dependent on the selection parameters such as solid solution (SS) phase, intermetallic (IM) compound and mixed phase (SS + IM).…”

Section: Phase Prediction and Electronic Interactionsmentioning

confidence: 99%

Emergence of machine learning in the development of high entropy alloy and their prospects in advanced engineering applications

Katiyar

Goel

2021

emergent mater.

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The high entropy alloys have become the most intensely researched materials in recent times. They offer the flexibility to choose a large array of metallic elements in the periodic table, a combination of which produces distinctive desirable properties that are not possible to be obtained by the pristine metals. Over the past decade, a myriad of publications has inundated the aspects of materials synthesis concerning HEA. Hitherto, the practice of HEA development has largely relied on a trial-and-error basis, and the hassles associate with this effort can be reduced by adopting a machine learning approach. This way, the “right first time” approach can be adopted to deterministically predict the right combination and composition of metallic elements to obtain the desired functional properties. This article reviews the latest advances in adopting machine learning approaches to predict and develop newer compositions of high entropy alloys. The review concludes by highlighting the newer applications areas that this accelerated development has enabled such that the HEA coatings can now potentially be used in several areas ranging from catalytic materials, electromagnetic shield protection and many other structural applications.

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Machine learning reveals the importance of the formation enthalpy and atom-size difference in forming phases of high entropy alloys

Cited by 88 publications

References 68 publications

Pitting Judgment Model Based on Machine Learning and Feature Optimization Methods

Pitting Judgment Model Based on Machine Learning and Feature Optimization Methods

Accelerated crystal structure prediction of multi-elements random alloy using expandable features

Emergence of machine learning in the development of high entropy alloy and their prospects in advanced engineering applications

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