High-throughput calculations in the context of alloy design
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Introduction Modern alloy design has evolved significantly from its historical roots as an Edisonian approach. Today, alloy development increasingly exploits the integration of sophisticated multimodal structural and chemical characterization, predictive computational modeling tools, and the acceleration of prototype sample processing. These developments have been described generally in reports covering the Integrated Computational Materials Engineering (ICME) framework and the Materials Genome Initiative.1–4 In alloy design, the approach enables the development of an advanced scientific understanding of the mechanisms underlying microstructure formation during processing, and the links between chemistry, structure, and the properties that are ultimately targeted for a specific engineering application. An essential ingredient is the integration of a hierarchy of computational tools facilitating development of comprehensive databases, furthering understanding, and guiding experimental efforts.5 Within this context, this article highlights the emergence of a novel tool for accelerated alloy design, high-throughput computing, broadly defined as robust workflows and software for interfacing computational tools to autonomously perform materials properties calculations on modern high-performance computers.6 The approach has had a significant impact in enabling combinatorial searches and the development of
property databases7–11 that can feed machine-learning models in materials discovery efforts. To date, most applications efforts have focused on properties of single-crystal, defect-free compounds. In contrast, the mechanical properties of engineering alloys are typically intimately tied to their defects and microstructures. As illustrated in Figure 1 for a ferritic superalloy,12 hierarchies of geometric and compositional variations across multiple length scales, featuring numerous multicomponent phases, with varying degrees of short- and long-range order can be involved. Control over these morphologies is required to achieve desired properties. An essential ingredient in this endeavor is the knowledge of the thermodynamic driving forces for structural evolution and phase stability. In this article, we describe recent progress in utilizing high-throughput computing to determine thermodynamic properties related to phase equilibria and transformations in alloys. We conclude with an outlook on opportunities to merge these approaches with state-of-the-art modeling of kinetic and interfacial properties that are essential for simulating microstructure evolution dynamics.
Overview of the CALculation of PHAse Diagram framework The CALculation of PHAse Diagram (CALPHAD) framework is a successful marriage of computational and experimental
Axel van de Walle, School of Engineering, Brown University, USA; [email protected] Mark Asta, Department of Materials Science and Engineering, University of California, Berkeley; and Materials Sciences Division, Lawrence Berkeley National Laboratory, USA; [email protected] doi:10.1
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