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Computational Discovery, Characterization, and Design of Single-Layer Materials

Single-layer materials open up tremendous opportunities for applications in nanoelectronic devices and energy technologies. We first review the four components of a materials science tetrahedron for single-layer materials. We then provide a theoretic

Volunteer computing for computational materials design

The problem of crystal structure prediction is very old and does, in fact, constitute the central problem of theoretical crystal chemistry. In this paper, we discuss the popular USPEX evolutionary algorithm for crystal structure prediction. Here we p

Computational materials design: A perspective for atomistic approaches

Present theoretical and computational approaches, combined with the impressive advances in computer hardware and software, open the possibility for materials design from first principles. This article presents a perspective on the relevant developmen

Development of a computational tool for materials design

An integrated modeling tool coupling thermodynamic calculation and kinetic simulation of multicomponent alloys is developed under the framework of integrated computational materials engineering. On the basis of Pandat™ software for multicomponent pha

Application-Specific Computational Materials Design via Multiscale Modeling and the Inductive Design Exploration Method (IDEM)

The development of materials is a laborious, iterative, expensive, and intuitive process, often requiring decades to transition from early laboratory studies to commercial applications. This research seeks to address this issue by demonstrating a sys

Metacognitive and computational aspects of chance discovery

Chance discovery is concerned with events or situations that affect human decision making; such events or situations are viewed as opportunities or risks. Perspectives are mental representations that describe partial knowledge of a task domain (cogni

Analysis of computational approaches for motif discovery

Recently, we performed an assessment of 13 popular computational tools for discovery of transcription factor binding sites (M. Tompa, N. Li, et al., "Assessing Computational Tools for the Discovery of Transcription Factor Binding Sites", Nature Biote

Improved benchmarks for computational motif discovery

An important step in annotation of sequenced genomes is the identification of transcription factor binding sites. More than a hundred different computational methods have been proposed, and it is difficult to make an informed choice. Therefore, robus

DOI: 10.1007/s11837-014-0887-1 Ó 2014 The Minerals, Metals & Materials Society

Computational Materials Discovery and Design MARK ASTA1,2 1.—Department of Materials Science and Engineering, University of California, Berkeley, CA 94720, USA. 2.—e-mail: [email protected]

The past decade has witnessed remarkable advances in the development of computational techniques for predictively modeling the links among materials processing, structure, and properties. These advances have given rise to the development of the field of integrated computational materials engineering (ICME), and they have been a driving force behind the establishment of the Materials Genome Initiative (MGI) in the United States, and related activities internationally. An overarching theme in these activities is to harness the power of modern computational hardware, in the application of state-of-the-art computational methods, to enable data-driven approaches to the accelerated design and development of new materials for targeted applications. The three articles in this short theme on computational materials discovery and design highlight various types of applications of computational modeling techniques to the process of materials development. At the initial, discovery stage, the methodology of high-throughput computations has enabled application of modern first-principles techniques as a framework for the development of materials databases that enable a materials designer to screen across a broad range of compounds (both known and yet-to-be synthesized) with variable chemistries and crystallographic structures, to search for materials with properties, or combinations of properties, required for optimal device performance (e.g., Refs. 1–3). The first article in this series, by Houlong L. Zhuang and Richard G. Hennig, titled ‘‘Computational Discovery, Characterization and Design of Single-Layer Materials,’’ provides an example of the use of first-principles computational methods in such a framework, targeting the discovery and characterization of ‘‘single-layer’’ materials that are being actively investigated for a range

Mark Asta is the guest editor for the Chemistry and Physics of Materials Committee, a joint committee of the TMS Electronic, Magnetic, & Photonic Materials Division (EMPMD) and the TMS Structural Metals Division (SMD); and coordinator of the topic Computational Materials Discovery in this issue.

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of nanoelectronic device and energy technology applications. The article provides an example of first-principles methods in the mode of materials discovery and highlights current challenges associated with the application of widely used methods for studies of two-dimensional (2D) materials derived from layered compounds. Beyond activities that aim to discover new compounds, materials design often involves the need to optimize composition and processing techniques to obtain desired microstructures, i.e., combinations of phases, phase fractions, and size distributions, required to achieve optimal properties and performance. This is particularly true in the design of multicomponent structural materials (e.g., Ref. 4). The remaining two articles in this series deal with such applications of computational tools in materials design. The second article, by P. E. A. Turchi, P. So¨derlind, and A. I. Landa, titled ‘‘From Electronic Structure to Thermodynamics of Actinide-Based Alloys,’’ provides a tutorial on how modern tools of first-principles calculations can be integrated with the framework of computational thermodynamics known as the CALPHAD method5 to model predictively phase stability in multicomponent systems. This article highlights applications of this approach to the challenging problem of designing metallic systems for nuclear fuel applications and ends with an example illustrating how such computational thermodynamic tools can be linked with global optimization algorithms to search multicomponent compositional space for alloys with desired phasestability characteristics (e.g., melting point and phase-fractions) for a targeted application. Finally, the last article in this set, by C. Zhang, W. Cao, S.-L. Chen, J. Zhu, F. Zhang, A. A. Luo, and R. Schmid-Fetzer, titled ‘‘Precipitation Simulation of AZ91 Alloy,’’ discusses the extension of CALPHAD-based techniques to the modeling of precipitate microstructures in age-hardened alloys. In this active field of research, significant progress has been realized over the past decade through the integration of CALPHAD thermodynamic dat-

(Published online February 16, 2014)

Computational Materials Discovery and Design

abases, with models and databases for diffusion kinetics in multicomponent systems, and efficient computational methods for modeling nucleation and growth kinetics (e.g., Refs. 6, 7). The article by Zhang et al. highlights the development and application of one such set of tools in the modeling of precipitation kinetics in Mg alloys, and it highlights the ways in which targeted experiments are required to enable the development and validation of such models for materials design. The contributions to this theme of Computational Materials Discovery and Design by no means represent a comprehensive coverage of this exciting area of materials science and engineering. Nevertheless, they hopefully provide a sense of the opportunities and remaining challenges inherent in exploiting computational methods to aid in accelerating the process of developing new materials, from

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the stages of discovery through design and development. REFERENCES 1. S. Curtarolo, G.L.W. Hart, M.B. Nardelli, N. Mingo, S. Sanvito, and O. Levy, Nat. Mater. 12, 191 (2013). 2. A. Jain, S.P. Ong, G. Hautier, W. Chen, W.D. Richards, S. Dacek, S. Cholia, D. Gunter, D. Skinner, G. Ceder, and K.A. Persson, APL Mater. 1, 011002 (2013). 3. Ceder and K. Persson, Sci. Amer 6, 309 (2013). 4. G.B. Olson, Acta Mater. 61, 771 (2013). 5. H.L. Lucas, S.G. Fries, and B. Sundman, Computational Thermodynamics—The CALPHAD Method (Cambridge, U.K.: Cambridge University Press, 2007). 6. U.R. Kattner and C.E. Campbell, Mater. Sci. Technol. 25, 443 (2009). 7. H.-J. Jou, P.W. Voorhees, and G.B. Olson, Superalloys 2004, ed. by K.A. Green, T.M. Pollock, H. Harada, T.E. Howson, R.C. Reed, J.J. Schirra, and S. Walston (Warrendale, PA: TMS, 2004), pp. 877–886.

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