CDS&E: Systematic Exploration of the High Entropy Alloy Space through High-Dimensional Thermodynamic Modeling from High-Throughput Computations and Experimental Data

Brown University, Providence RI

Investigators

Abstract

Nontechnical summary The field of materials science has been captivated by the discovery of a class of alloys known as "high entropy" alloys, which are characterized by the unexpected stabilization of simple crystal structures through the combination of a large number of different elements in comparable amounts. The discovery and design of such alloys demand a detailed knowledge of the thermodynamic factors governing stability in a very high dimensional composition space with potentially many competing crystal structures. This complexity tests the limits of current thermodynamic modeling capabilities due to the sheer number of input data needed and of parameters entering the model. The project addresses this by combining (i) large-scale meta-databases of experimentally-derived thermodynamic models with (ii) thermodynamic data from high-throughput quantum-mechanical calculations, using formal statistical and active machine learning techniques. The end product of this effort is an openly distributed large-scale encompassing thermodynamic model that can be queried in a high-dimensional compositional space and that returns structural stability information as interactive tridimensional cross-sections, or as composition- and temperature-dependent thermodynamic properties. As this space of possible alloys is so vast compared to the number of known high-entropy alloys, the potential for discoveries of novel alloys through this tool is significant and this could broadly impact numerous engineering applications where capabilities are limited by materials properties. Technical summary The project leverages and integrates two recent developments from the PI's group: (i) a search engine (the Thermodynamic DataBase DataBase or TDBDB), that indexes all available experimentally-derived thermodynamic data electronically available in standardized format in the scientific literature; (ii) a suite of software tools (the Alloy Theoretic Automated Toolkit or ATAT) that streamlines the generation of thermodynamic databases from ab initio data. This hybrid approach aims to combine the distinct advantage of state-of-the-art experimental and computational methods, namely, the higher accuracy of the former and the high-throughput nature of the latter. Active machine learning and statistical techniques are used to (i) target the exploration of promising composition regions likely to form solid solutions with simple crystal structures and (ii) develop efficient statistical mechanics models of non-stoichiometric solids that require few ab initio inputs, thus enabling "high-throughput" operation. Whereas existing computational high-throughput efforts primarily focus on the properties of defect-free stoichiometric compounds at absolute zero, this project targets, at all temperatures, the broader range of materials including disordered alloys with possible short-range order and ordered alloys with possible point defects. This demands efficient statistical mechanical models of (i) short-range order, (ii) strongly anharmonic phases and (iii) magnetic ordering, each of which requiring fewer ab initio input than existing brute force methods, by exploiting both known and data-mined trends. The data and tools devised during this project are expected to have broader impacts: virtually all engineering materials are nonstoichiometric alloys whose properties are tuned by controlled additions of numerous components (with high entropy alloys only representing an extreme example). Hence, this resource could greatly facilitate materials discovery and optimization by augmenting existing computational high-throughput efforts that currently only target ordered compounds at absolute zero. The broad compositional gamut covered also enables applications that could range from identifying glass-forming metallic alloys to determining possible exoplanet core compositions and structures. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.