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

• Published in: Integrating materials and manufacturing innovation Vol. 10; no. 4; pp. 610 - 626
• Main Authors: Reddy, G. Jaideep, Kandavalli, Manjunadh, Saboo, Tanay, Rao, A. K. Prasada
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.12.2021; Springer Nature B.V
• Subjects: Algorithms; Amorphous alloys; Amorphous materials; Artificial neural networks; Characterization and Evaluation of Materials; Chemistry and Materials Science; Composition; Corrosion resistance; Corrosive wear; Enthalpy; Glass formation; Learning theory; Machine learning; Materials Science; Metallic glasses; Metallic Materials; Modulus of elasticity; Nanotechnology; Neural networks; Structural Materials; Surfaces and Interfaces; Technical Article; Thickness; Thin Films; Wear resistance; Simulation and modelling; Metals and alloys; Amorphous materials; Machine-learning; Bulk metallic glasses; Glass forming ability
• ISSN: 2193-9764; 2193-9772
• DOI: 10.1007/s40192-021-00239-y

Bulk metallic glass has been a fascinating class of metallic systems with remarkable corrosion resistance, elastic modulus, and wear resistance, while evaluating the glass forming ability has been a very interesting aspect for decades. Machine learning techniques, viz., artificial neural networks and KNearest Regressor-based models have been developed in this work to predict the glass forming ability, given the composition of the bulk metallic glassy alloy. A new criterion of classification of atoms present in a bulk metallic glass is proposed. Feature importance analysis confirmed that the accuracy of the prediction depends mainly on change in enthalpy of mixing and change in entropy of mixing. However, among the artificial neural network models and KNearest Regressor models developed, the former showed a promising performance in prediction of the glass formation ability (critical thickness). It has been successfully demonstrated and validated with experimental critical thickness that the glass forming ability can be predicted using an artificial neural network given the elemental composition alone. A computational algorithm was also developed to classify the atoms as big/small in each given alloy. The outcome of this algorithm is used as input parameters to the ANN and other machine learning models used in this work.