Phase Prediction in High Entropy Alloys by Various Machine Learning Modules Using Thermodynamic and Configurational Parameters

Mandal, Pritam; Choudhury, Amitava; Mallick, Amitava Basu; Ghosh, Manojit

doi:10.1007/s12540-022-01220-w

Cited by 27 publications

(1 citation statement)

References 47 publications

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“…This suggests that it is possible to achieve good phase classification results by simply using combinations of elements to build ML models. Mandal et al [24] utilized the SVM algorithm to fit five thermodynamic, configurational, and electronic parameters to these parameters. Classification accuracy of 93.84% was achieved for SS, amorphous intermetallic compounds, and 84.32% for BCC, FCC, and their mixed phases.…”

Section: Introductionmentioning

confidence: 99%

A New Phase Classifier with an Optimized Feature Set in ML-Based Phase Prediction of High-Entropy Alloys

Zhang,

Ren,

Wang

et al. 2023

Applied Sciences

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The phases of high-entropy alloys (HEAs) are closely related to their properties. However, phase prediction bears a significant challenge due to the extensive search space and complex formation mechanisms of HEAs. This study demonstrates a precise and timely methodology for predicting alloy phases. It first developed a machine learning classifier using 145 features and a dataset with 1009 samples to differentiate the four types of alloy phases. Feature selection was performed on the feature set using an Embedded algorithm and a genetic algorithm, resulting in the selection of nine features. The Light GBM algorithm was chosen to train the machine learning model. Finally, the implementation of oversampling and cost-sensitive methods enables LightGBM to tackle the problem of insufficient accuracy in BCC+FCC phase classification. The resulting accuracy of the alloy phase prediction model, evaluated through ten-fold cross-validation, stands at 0.9544.

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Section: Introductionmentioning

confidence: 99%

A New Phase Classifier with an Optimized Feature Set in ML-Based Phase Prediction of High-Entropy Alloys

Zhang,

Ren,

Wang

et al. 2023

Applied Sciences

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Experimental and Computational Study of Microstructure of Al2FeCoNiCu High-Entropy Alloy

Kivy

Mahata

Thompson

et al. 2023

J. Phase Equilib. Diffus.

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Exploring Multicomponent Phase Space to Discover New Materials

Cantor

2024

J. Phase Equilib. Diffus.

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Multicomponent phase space has been shown to consist of an enormous number of materials with different compositions, the vast majority of which have never been made or investigated, with great potential, therefore, for the discovery of exciting new materials with valuable properties. At the same time, however, the enormous size of multicomponent phase space makes it far from straightforward to identify suitable strategies for exploring the plethora of potential material compositions and difficult, therefore, to be successful in discovering desirable new materials. Unfortunately, all our knowhow and understanding has been developed for materials with relatively few components in relatively limited proportions, with most of our scientific theories relying essentially on linear assumptions of component dilution and independence that no longer apply in concentrated multicomponent materials. Trial and error, controlled substitution, parameterisation, thermodynamic modelling, atomistic modelling and machine learning techniques have all been employed as methods of exploring multicomponent phase space, with varying levels of success, but ultimately none of these techniques has proved capable of delivering consistent or guaranteed results. This paper provides an overview of the different techniques that have been used to explore multicomponent phase space, indicates their main advantages and disadvantages, and describes some of their successes and failures.

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Phase Prediction in High Entropy Alloys by Various Machine Learning Modules Using Thermodynamic and Configurational Parameters

Cited by 27 publications

References 47 publications

A New Phase Classifier with an Optimized Feature Set in ML-Based Phase Prediction of High-Entropy Alloys

A New Phase Classifier with an Optimized Feature Set in ML-Based Phase Prediction of High-Entropy Alloys

Experimental and Computational Study of Microstructure of Al2FeCoNiCu High-Entropy Alloy

Exploring Multicomponent Phase Space to Discover New Materials

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