Three-dimensional structural stability diagrams for 648 binary AB3 and 389 binary A3B5 intermetallic compounds: III

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

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.

Structure maps for A I 4 A II 6(BO4)6X2 apatite compounds via data mining

This paper describes a method to identify key crystallographic parameters that can serve as strong classifiers of crystal chemistries and hence define new structure maps. The selection of this pair of key parameters from a large set of potential classifiers is accomplished through a linear data-dimensionality reduction method. A multivariate data set of known A I 4 A II 6(BO4)6 X 2 apatites is used as the basis for the study where each A I 4 A II 6(BO4)6 X 2 compound is represented as a 29-dimensional vector, where the vector components are discrete scalar descriptors of electronic and crystal structure attributes. A new structure map, defined using the two distortion angles αAII (rotation angle of A II—A II—A II triangular units) and ψAIz = 0 AI—O1 (angle the A I—O1 bond makes with the c axis when z = 0 for the A I site), is shown to classify apatite crystal chemistries based on site occupancy on the A, B and X sites. The classification is accomplished using a K-means clustering analysis.

Structure Maps and Phase Stability in AlTi3 Alloyed with Rare-Earth Elements

Rare-earth elements including Y, Er and Sc were added to AlTi3 for stabilizing the Ll2 ordered crystal structure, as predicted by the AB3 structure map. The crystal structure and phase composition in the AlTi3 alloys were studied by electron microprobe analysis, X-ray diffraction and TEM. The solubility limit of the rare-earth elements were determined and correlated with the atomic size factor. The results obtained so far indicate that rare-earth additions are unable to change the crystal structure of AlTi3 from DO19 to Ll2. The inability to stabilize the Ll2 structure demonstrates the need to characterize the structure map domains with a further period-dependent parameter.

Intermetallic Compounds for High-Temperature Structural Use

The next generation of efficient turbines and engines will require materials that can withstand operating temperatures approaching 2000 degrees C. Intermetallic compounds with high melting temperatures are candidates for this application, but the obstacle of their limited ductility must first be overcome. Because the available data on these materials is limited, a survey of the effects of chemistry and crystal structure must be performed. With the use of melting temperature and density as figures of merit, the most likely candidates have been identified for preliminary screening.