Mapping Chemical Selection Pathways for Designing Multicomponent Alloys: an informatics framework for materials design

A data driven methodology is developed for tracking the collective influence of the multiple attributes of alloying elements on both thermodynamic and mechanical properties of metal alloys. Cobalt-based superalloys are used as a template to demonstrate the approach. By mapping the high dimensional nature of the systematics of elemental data embedded in the periodic table into the form of a network graph, one can guide targeted first principles calculations that identify the influence of specific elements on phase stability, crystal structure and elastic properties. This provides a fundamentally new means to rapidly identify new stable alloy chemistries with enhanced high temperature properties. The resulting visualization scheme exhibits the grouping and proximity of elements based on their impact on the properties of intermetallic alloys. Unlike the periodic table however, the distance between neighboring elements uncovers relationships in a complex high dimensional information space that would not have been easily seen otherwise. The predictions of the methodology are found to be consistent with reported experimental and theoretical studies. The informatics based methodology presented in this study can be generalized to a framework for data analysis and knowledge discovery that can be applied to many material systems and recreated for different design objectives

Structure maps for MAX phases formability revisited

The extraordinary chemical diversity of MAX phases raises the question of how many and which novel ones are yet to be discovered. The conventional schemes rely either on executions of well designed experiments or elaborately crafted calculations; both of which have been key tactics within the past several decades that have yielded many of important new materials we are studying and using today. However, these approaches are expensive despite the emergence of high throughput automations or evolution of high speed computers. In this work, we have revisited the in prior proposed light duty strategy, i.e. structure mapping, for describing the genomic conditions under which one MAX phase could form; that allow us to make successful formability and non formability separation of MAX phases with a fidelity of 95.5%. Our results suggest that the proposed coordinates, and further the developed structure maps, are able to offer a useful initial guiding principles for systematic screenings of potential MAX phases and provide untapped opportunities for their structure prediction and materials design

Understanding the Structure of Materials at the Intersection of Rationalisation, Prediction and Big Data

Theoretical materials science has a large and growing role to play in modern society thanks to its ability to deliver materials with new and interesting properties. The properties of any material are, on some level, a function of its internal structure. In this work we combine three important tools spanning the last 100 years of materials research, rationalisation, prediction and big data in an attempt to understand the factors that underpin the stability of ordered structures and to build an understanding of structure that is agnostic of a particular element or building block. We apply rationalisation to data mining of the Inorganic Crystal Structure Database, using various proposed structure descriptors to probe the factors affecting structure stability. Extensive prediction is performed on the Fe-Ni-Si system at inner earth core pressures to determine the phases most likely to be present, yielding a new, stable, Ni-Si structure. A new prediction technique for 2D grain boundaries is presented that doubles the size of system that can reasonably be studied at the ab initio level of theory. The structurally rich phosphorus and arsenic systems are investigated using structure prediction, producing new metastable structures. Finally, we use a simple model for particles that attract at long range and repel at short to probe all the possible binary structures over a wide range of stoichiometries. By carrying out prediction over a wide range of potential parameters we build a database of almost 20M entries. Contained within are a number of unreported structures including many in parts of parameter space that go beyond the periodic table in terms of size and bond energy ratios. Our work provides hints that these hypothetical structures could be realised in self assembling systems made up from constituents with tunable interactions opening the door to the possibility of new properties