Prediction of Glass Forming Ability of Bulk Metallic Glasses Using Machine Learning

Reddy, G. Jaideep; Kandavalli, Manjunadh; Saboo, Tanay; Rao, A.K. Prasada

doi:10.1007/s40192-021-00239-y

Cited by 8 publications

(4 citation statements)

References 13 publications

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“…Mastropietro et al used multiple linear regression and tree boosting to predict the maximum amorphous diameter of Fe-based BMGs; the R 2 value was increased from 0.71 to 0.90 after training [69]. Reddy et al predicted the glass-forming ability of a BMG by ML based on elemental composition alone [70]. Schultz et al tried to link characteristic temperature and glassforming ability in BMGs by ML; it was found that the critical cooling rate (R c ) might be a better target for machine learning model prediction than critical casting diameter (D c ) [71].…”

Section: Potential Composition Design Methods For Heaasmentioning

confidence: 99%

Composition Design Strategy for High Entropy Amorphous Alloys

Ding,

Zhang,

Yao

2024

Materials

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High entropy amorphous alloys (HEAAs) are materials that have received much attention in recent years. They exhibit many unique properties; however, research on their composition design method has not been deep enough. In this paper, we summarized some effective composition design strategies for HEAAs. By adjusting the atomic ratio from quinary bulk metallic glasses, Ti20Zr20Cu20Ni20Be20 HEAA with a high fracture strength of 2315 MPa was designed. By similar element addition/substitution, a series of Ti–(Zr, Hf, Nb)–Cu–Ni–Be HEAAs was developed. They possess good glass-forming ability with a maximum critical diameter of 30 mm. Combining elements from those ternary/quaternary bulk metallic glasses has also proved to be an effective method for designing new HEAAs. The effect of high entropy on the property of the alloy, possible composition design methods, and potential applications were also discussed. This paper may provide helpful inspiration for future development of HEAAs.

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Section: Potential Composition Design Methods For Heaasmentioning

confidence: 99%

Composition Design Strategy for High Entropy Amorphous Alloys

Ding,

Zhang,

Yao

2024

Materials

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“…In the model, the elements from a given set and final required phases of HEA are taken as the input parameters and then, the algorithm, through a combination of ML models (HEA classifier and BM regressor), predicts the most favourable elemental composition to optimize the bulk-modulus for the HEA. The HEA classifier was trained by using data from the research work of Dam et al 29 and Reddy et al 30 . The data used for the prediction of bulk modulus values for the HEA cluster was taken from Precker et al 31 and Mishra et al 32 .…”

Section: Pre-processingmentioning

confidence: 99%

Design of high bulk moduli high entropy alloys using machine learning

Kandavalli,

Agarwal,

Poonia

et al. 2023

Sci Rep

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In this work, the authors have demonstrated the use of machine learning (ML) models in the prediction of bulk modulus for High Entropy Alloys (HEA). For the first time, ML has been used for optimizing the composition of HEA to achieve enhanced bulk modulus values. A total of 12 ML algorithms were trained to classify the elemental composition as HEA or non-HEA. Among these models, Gradient Boosting Classifier (GBC) was found to be the most accurate, with a test accuracy of 78%. Further, six regression models were trained to predict the bulk modulus of HEAs, and the best results were obtained by LASSO Regression model with an R-square value of 0.98 and an adjusted R-Square value of 0.97 for the test data set. This work effectively bridges the gap in the discovery and property analysis of HEAs. By accelerating material discovery via providing alternate means for designing virtual alloy compositions having favourable bulk modulus for respective applications, this work opens new avenues of applications of HEAs.

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“…These approaches enable researchers to develop models that can accurately predict the GFA of new compositions, thereby reducing the need for extensive experimentation. Currently, most GFA predictions are based on estimating the critical diameter (D max ) [6][7][8][9][10][11][12][13]. While D max provides a direct measure of GFA, it is influenced by various factors, including fabrication processes, sample shape, and testing methods.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Aided Prediction of Glass-Forming Ability of Metallic Glass

Liu,

Wang,

Cai

et al. 2023

Processes

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The prediction of the glass-forming ability (GFA) of metallic glasses (MGs) can accelerate the efficiency of their development. In this paper, a dataset was constructed using experimental data collected from the literature and books, and a machine learning-based predictive model was established to predict the GFA. Firstly, a classification model based on the size of the critical diameter (Dmax) was established to determine whether an alloy system could form a glass state, with an accuracy rating of 0.98. Then, regression models were established to predict the crystallization temperature (Tx), glass transition temperature (Tg), and liquidus temperature (Tl) of MGs. The R2 of the prediction model obtained in the test set was greater than 0.89, which showed that the model had good prediction accuracy. The key features used by the regression models were analyzed using variance, correlation, embedding, recursive, and exhaustive methods to select the most important features. Furthermore, to improve the interpretability of the prediction model, feature importance, partial dependence plot (PDP), and individual conditional expectation (ICE) methods were used for visualization analysis, demonstrating how features affect the target variables. Finally, taking Zr-Cu-Ni-Al system MGs as an example, a prediction model was established using a genetic algorithm to optimize the alloy composition for high GFA in the compositional space, achieving the optimal design of alloy composition.

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Prediction of Glass Forming Ability of Bulk Metallic Glasses Using Machine Learning

Cited by 8 publications

References 13 publications

Composition Design Strategy for High Entropy Amorphous Alloys

Composition Design Strategy for High Entropy Amorphous Alloys

Design of high bulk moduli high entropy alloys using machine learning

Machine Learning Aided Prediction of Glass-Forming Ability of Metallic Glass

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