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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)

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 geometry-independent way. The key concept is to use the quotient group associated with each superlattice to determine all unique atomic configurations.… (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 hexagonal-close-packed ͑hcp͒ alloys. Extending the work of Hart and Forcade ͓Phys. Rev. B 77, 224115 ͑2008͔͒ ͑which applies only to Bravais lattices͒, this… (More)

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)

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)

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)

Predicting from first-principles calculations whether mixed metallic elements phase-separate or form ordered structures is a major challenge of current materials research. It can be partially addressed in cases where experiments suggest the underlying lattice is conserved, using cluster expansion (CE) and a variety of exhaustive evaluation or genetic search… (More)

Rhenium is an important alloying agent in catalytic materials and superalloys, but the experimental and computational data on its binary alloys are sparse. Only 6 out of 28 Re transition-metal systems are reported as compound-forming. Fifteen are reported as phase-separating, and seven have high-temperature disordered σ or χ phases. Comprehensive… (More)

The ability to predict the existence and crystal type of ordered structures of materials from their components is a major challenge of current materials research. Empirical methods use experimental data to construct structure maps and make predictions based on clustering of simple physical parameters. Their usefulness depends on the availability of reliable… (More)

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)