High-throughput calculations in the context of alloy design
TL;DR: In this paper, the authors present a review of recent developments and applications in this area, and discuss future opportunities for high-throughput calculations in the context of modeling kinetics, highlighting the important role of interfacial processes and atomic mobilities.
Abstract: Modern approaches to alloy design increasingly exploit the framework of computational thermodynamics and kinetics to guide the selection of alloy compositions and processing strategies, to achieve desired microstructures, and yield tailored properties. In this context, phase diagrams play a critical role and their assessment can represent a bottleneck in the design of new multicomponent systems. In recent years, it has become possible to accelerate this process through the coupling of the CALculation of PHAse Diagram (CALPHAD) computational thermodynamics framework with high-throughput quantum mechanical calculations. This article reviews recent developments and applications in this area, and discusses future opportunities for high-throughput calculations in the context of modeling kinetics, highlighting the important role of interfacial processes and atomic mobilities.
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TL;DR: The current status of the interatomic potential field, comparing the strengths and weaknesses of the traditional and ML potentials, is reviewed in this paper, where a new class of potentials is introduced, in which an ML model is coupled with a physics-based potential to improve the transferability to unknown atomic environments.
75 citations
TL;DR: In this paper , the influence of scrap-related impurities on the thermodynamics and kinetics of precipitation reactions and their mechanical and electrochemical effects; impurity effects on precipitation-free zones around grain boundaries; their effects on casting microstructures; and the possibilities presented by adjusting processing parameters and the associated mechanical, functional and chemical properties.
51 citations
01 Jan 2015
TL;DR: In this paper, the authors provide a current snapshot of the rapidly evolving field of computational materials design and highlight the challenges and opportunities that lie ahead, as well as the current state of the art.
Abstract: High-throughput computational materials design is an emerging area of materials science. By combining advanced thermodynamic and electronic-structure methods with intelligent data mining and database construction, and exploiting the power of current supercomputer architectures, scientists generate, manage and analyse enormous data repositories for the discovery of novel materials. In this Review we provide a current snapshot of this rapidly evolving field, and highlight the challenges and opportunities that lie ahead.
48 citations
TL;DR: In this paper, the authors highlight progress in theory, computation, data, and advanced experimental techniques that are advancing our capabilities for rapid discovery and design of new multicomponent alloys.
Abstract: The discovery, design, and development of new alloys have long been critical elements of advanced engineering systems. Challenged by their chemical and structural complexity, this design process is, however, often too slow. This article highlights progress in theory, computation, data, and advanced experimental techniques that are advancing our capabilities for rapid discovery and design of new multicomponent alloys. Applied across the length scales, these new capabilities support exploration across broad composition spaces; examples of new materials and associated advances in the understanding of underlying thermochemical and thermomechanical phenomena are presented. We highlight current challenges, gaps, and specific areas that, if further developed, could have future high payoff.
22 citations
TL;DR: An easy-to-use, versatile, and open-source data analytics frontend, ASCENDS (Advanced data SCiENce toolkit for Non-Data Scientists), designed with the intent of accelerating data-driven materials research and development is introduced.
Abstract: Emerging modern data analytics attracts much attention in materials research and shows great potential for enabling data-driven design. Data populated from the high-throughput CALPHAD approach enables researchers to better understand underlying mechanisms and to facilitate novel hypotheses generation, but the increasing volume of data makes the analysis extremely challenging. Herein, we introduce an easy-to-use, versatile, and open-source data analytics frontend, ASCENDS (Advanced data SCiENce toolkit for Non-Data Scientists), designed with the intent of accelerating data-driven materials research and development. The toolkit is also of value beyond materials science as it can analyze the correlation between input features and target values, train machine learning models, and make predictions from the trained surrogate models of any scientific dataset. Various algorithms implemented in ASCENDS allow users performing quantified correlation analyses and supervised machine learning to explore any datasets of interest without extensive computing and data science background. The detailed usage of ASCENDS is introduced with an example of experimental high-temperature alloy data.
18 citations
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TL;DR: The Materials Project (www.materialsproject.org) is a core program of the Materials Genome Initiative that uses high-throughput computing to uncover the properties of all known inorganic materials as discussed by the authors.
Abstract: Accelerating the discovery of advanced materials is essential for human welfare and sustainable, clean energy. In this paper, we introduce the Materials Project (www.materialsproject.org), a core program of the Materials Genome Initiative that uses high-throughput computing to uncover the properties of all known inorganic materials. This open dataset can be accessed through multiple channels for both interactive exploration and data mining. The Materials Project also seeks to create open-source platforms for developing robust, sophisticated materials analyses. Future efforts will enable users to perform ‘‘rapid-prototyping’’ of new materials in silico, and provide researchers with new avenues for cost-effective, data-driven materials design. © 2013 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution 3.0 Unported License.
6,566 citations
TL;DR: In this article, the authors present the data for the condensed phases of 78 elements as currently used by SGTE (Scientific Group Thermodata Europe) as a sound basis for the critical assessment of thermodynamic data, thereby, perhaps, limiting unnecessary duplication of effort.
Abstract: Thermodynamic data for the condensed phases of 78 elements as currently used by SGTE (Scientific Group Thermodata Europe) are tabulated. SGTE is a consortium of seven organisations in Western Europe engaged in the compilation of a comprehensive, self consistent and authoritative thermochemical database for inorganic and metallurgical systems. The data are being published here in the hope that they will become widely adopted within the international community as a sound basis for the critical assessment of thermodynamic data, thereby, perhaps, limiting unnecessary duplication of effort. The data for each phase of each element considered aie presented as expressions showing, as a function of temperature, the variation of (a) G-HSER, the Gibbs energy relative to the enthalpy of the “Standard Element Reference” ie the reference phase for the element at 298.15 K and (b) the difference in Gibbs energy between each phase and this reference phase (ie lattice stability). The variation of the heat capacity of the various phases and the Gibbs energy difference between phases are also shown graphically. For certain elements the thermodynamic data have been assessed as a function of pressure as well as temperature. Where appropriate a temperature— pressure phase diagram is also shown.
Throughout this paper the thermodynamic data are expressed in terms of J mol−1. The temperatures of transition between phases have been assessed to be consistent with the 1990 International Temperature Scale (ITS90).
4,116 citations
TL;DR: It is shown how advanced thermodynamic calculations have become more accessible since: - A more user-friendly windows version of Thermo-Calc, TCW, has been developed, and there is an increasing amount of thermodynamic databases for different materials available.
Abstract: Software for calculation of phase diagrams and thermodynamic properties have been developed since the 1970's. Software and computers have now developed to a level where such calculations can be used as tools for material and process development. In the present paper some of the latest software developments at Thermo-Calc Software are presented together with application examples. It is shown how advanced thermodynamic calculations have become more accessible since: - A more user-friendly windows version of Thermo-Calc, TCW, has been developed. - There is an increasing amount of thermodynamic databases for different materials available. - Thermo-Calc can be accessed from user-written software through several different programming interfaces are available which enables access to the thermodynamic software from a user-written software. Accurate data for thermodynamic properties and phase equilibria can then easily be incorporated into software written in e.g. C++, Matlab and FORTRAN. Thermo-Calc Software also produces DICTRA, a software for simulation of diffusion controlled phase transformations. Using DICTRA it is possible to simulate processes such as homogenization, carburising, microsegregation and coarsening in multicomponent alloys. The different models in the DICTRA software are briefly presented in the present paper together with some application examples.
3,186 citations
TL;DR: It is shown that it is possible to design special quasirandom structures'' (SQS) that mimic for small {ital N} the first few, physically most relevant radial correlation functions of a perfectly random structure far better than the standard technique does.
Abstract: Structural models used in calculations of properties of substitutionally random ${\mathit{A}}_{1\mathrm{\ensuremath{-}}\mathit{x}}$${\mathit{B}}_{\mathit{x}}$ alloys are usually constructed by randomly occupying each of the N sites of a periodic cell by A or B. We show that it is possible to design ``special quasirandom structures'' (SQS's) that mimic for small N (even N=8) the first few, physically most relevant radial correlation functions of a perfectly random structure far better than the standard technique does. We demonstrate the usefulness of these SQS's by calculating optical and thermodynamic properties of a number of semiconductor alloys in the local-density formalism.
2,545 citations
TL;DR: A current snapshot of high-throughput computational materials design is provided, and the challenges and opportunities that lie ahead are highlighted.
Abstract: High-throughput computational materials design is an emerging area of materials science. By combining advanced thermodynamic and electronic-structure methods with intelligent data mining and database construction, and exploiting the power of current supercomputer architectures, scientists generate, manage and analyse enormous data repositories for the discovery of novel materials. In this Review we provide a current snapshot of this rapidly evolving field, and highlight the challenges and opportunities that lie ahead.
1,568 citations