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We present an algorithm for generating all derivative superstructures—for arbitrary parent structures and for any number of atom types. This algorithm enumerates superlattices and atomic configurations in a geometryindependent way. The key concept is to use the quotient group associated with each superlattice to determine all unique atomic configurations.… (More)

The cluster expansion method provides a standard framework to map first-principles generated energies for a few selected configurations of a binary alloy onto a finite set of pair and many-body interactions between the alloyed elements. These interactions describe the energetics of all possible configurations of the same alloy, which can hence be readily… (More)

- Gus L W Hart, Volker Blum, Michael J Walorski, Alex Zunger
- Nature materials
- 2005

Modern condensed-matter theory from first principles is highly successful when applied to materials of given structure-type or restricted unit-cell size. But this approach is limited where large cells or searches over millions of structure types become necessary. To treat these with first-principles accuracy, one 'coarse-grains' the many-particle… (More)

We present an algorithm for generating all derivative superstructures of a nonprimitive parent lattice. The algorithm has immediate application in important materials design problems such as modeling hexagonalclose-packed hcp alloys. Extending the work of Hart and Forcade Phys. Rev. B 77, 224115 2008 which applies only to Bravais lattices , this approach… (More)

We present an algorithm for generating derivative superstructures for large unit cells at a fixed concentration. The algorithm is useful when partial crystallographic information of an ordered phase is known. This work builds on the previous work of Hart and Forcade [Phys. Rev. B 77 224115, 2008; Phys. Rev. B 80 014120, 2009]. This extension of the original… (More)

Long-standing challenges in cluster expansion (CE) construction include choosing how to truncate the expansion and which crystal structures to use for training. Compressive sensing (CS), which is emerging as a powerful tool for model construction in physics, provides a mathematically rigorous framework for addressing these challenges. A recently-developed… (More)

- Stefano Curtarolo, Gus L W Hart, Marco Buongiorno Nardelli, Natalio Mingo, Stefano Sanvito, Ohad Levy
- Nature materials
- 2013

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… (More)

The widely accepted intuition that the important properties of solids are determined by a few key variables underpins many methods in physics. Though this reductionist paradigm is applicable in many physical problems, its utility can be limited because the intuition for identifying the key variables often does not exist or is difficult to develop. Machine… (More)

- Ohad Levy, Roman V Chepulskii, Gus L W Hart, Stefano Curtarolo
- Journal of the American Chemical Society
- 2010

The experimental and computational data on rhodium binary alloys is sparse despite its importance in numerous applications, especially as an alloying agent in catalytic materials. Half of the Rh-transition metal systems (14 out of 28) are reported to be phase separating or are lacking experimental data. Comprehensive high-throughput first-principles… (More)

- Gus L W Hart
- Nature materials
- 2007

Our society's environmental and economic progress depends on the development of high-performance materials such as lightweight alloys, high-energy-density battery materials, recyclable motor vehicle and building components, and energy-efficient lighting. Meeting these needs requires us to understand the central role of crystal structure in a material's… (More)