An informatics guided classification of miscible and immiscible binary alloy systems

R F Zhang,S H Sheng,S Srinivasan,T C Germann,S H Zhang,H T Wang,X F Kong,K Rajan,D Legut

doi:10.1038/s41598-017-09704-1

Abstract

The classification of miscible and immiscible systems of binary alloys plays a critical role in the design of multicomponent alloys. By mining data from hundreds of experimental phase diagrams, and thousands of thermodynamic data sets from experiments and high-throughput first-principles (HTFP) calculations, we have obtained a comprehensive classification of alloying behavior for 813 binary alloy systems consisting of transition and lanthanide metals. Among several physics-based descriptors, the slightly modified Pettifor chemical scale provides a unique two-dimensional map that divides the miscible and immiscible systems into distinctly clustered regions. Based on an artificial neural network algorithm and elemental similarity, the miscibility of the unknown systems is further predicted and a complete miscibility map is thus obtained. Impressively, the classification by the miscibility map yields a robust validation on the capability of the well-known Miedema’s theory (95% agreement) and shows good agreement with the HTFP method (90% agreement). Our results demonstrate that a state-of-the-art physics-guided data mining can provide an efficient pathway for knowledge discovery in the next generation of materials design.

Highlights

The purpose of the Materials Genome Initiative (MGI) is to accelerate the discovery of novel materials by means of modern computational techniques and data mining methods[1,2,3]
Because of the scarcity of experimental and high-throughput first-principles (HTFP) data for binary alloy systems consisting of binary lanthanide metals, i.e. RE-RE, we shall not discuss this group except for three binary systems of Sc-Y, Sc-La and La-Y
We found 35 binary alloy systems where a disagreement appeared between phase diagram analysis and HTFP calculations

Summary

Introduction

The purpose of the Materials Genome Initiative (MGI) is to accelerate the discovery of novel materials by means of modern computational techniques and data mining methods[1,2,3]. In the fields of physical metallurgy, vast new data have been recently collected and reported for the mixing properties of binary alloy systems. This provides a unique opportunity to construct a visual miscibility map for binary alloy systems by mining these large numbers of data sources. A binary alloy system may readily form intermetallic compounds, or a homogeneous or clustered solid solution, typically with a solubility of about 10%5, 12–14. For such miscible cases, the alloying process is exothermic, with a negative formation enthalpy (e.g., Cu-Ti system)[15].

Methods

Results

Conclusion