Probabilistic Assessment of Glass Forming Ability Rules for Metallic Glasses Aided by Automated Analysis of Phase Diagrams

Aparajita Dasgupta,Ramachandran Subramanian,Venu Govindaraju,Bhargava U Kota,Krishna Rajan,Connor Mack,Srirangaraj Setlur,Scott R Broderick

doi:10.1038/s41598-018-36224-3

Abstract

The use of machine learning techniques to expedite the discovery and development of new materials is an essential step towards the acceleration of a new generation of domain-specific highly functional material systems. In this paper, we use the test case of bulk metallic glasses to highlight the key issues in the field of high throughput predictions and propose a new probabilistic analysis of rules for glass forming ability using rough set theory. This approach has been applied to a broad range of binary alloy compositions in order to predict new metallic glass compositions. Our data driven approach takes into account not only a broad variety of thermodynamic, structural and kinetic based criteria, but also incorporates qualitative and descriptive attributes associated with eutectic points in phase diagrams. For the latter, we demonstrate the use of automated machine learning methods that go far beyond text recognition approaches by also being able to interpret phase diagrams. When combined with structural descriptors, this approach provides the foundations to develop a hierarchical probabilistic predication tool that can rank the feasibility of glass formation.

Highlights

A long standing problem in identifying potential binary alloy chemistries that can be good candidates for metallic glass formation has been the challenge of finding a unified thermochemical, structural and kinetic criteria for glass formability[2,3]
A key component in predicting new metallic glasses has been to identify deep eutectic compositions indicating the importance of analyzing phase diagram information
We have previously demonstrated our automated phase diagram reading methodology[23], which correctly classified phases and associate phase labels with 94% accuracy, and which we demonstrated with the capability to identify and characterize eutectic points

Summary

Introduction

A long standing problem in identifying potential binary alloy chemistries that can be good candidates for metallic glass formation has been the challenge of finding a unified thermochemical, structural and kinetic criteria for glass formability[2,3]. Cao et al for instance, showed recently that the glass transition temperature, Tg of a variety of metallic glasses has a close relationship with the eutectic and peritectic points within binary phase diagrams[16] In addition to these thermodynamic metrics, certain empirical rules have been proposed for glass formability. From their work, which largely utilized formation enthalpies and additional metrics related to similarity to predict glass formation, they concluded that the number of systems which may be glass formers is far larger than previously thought, with more than 17% of binary alloys serving as potential glass formers This motivates the current study, which is to provide a clear design rule for glass formers, while building on the previous analyses by defining the compositional ranges which are glass formers. A summary of the various design rules (thermochemical, structural, and kinetic) for glass formability is provided in the supplementary material

Results

Discussion

Conclusion