Gus L. W. Hart

The high-throughput highway to computational materials design

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.

First-principles elastic constants and electronic structure of α-Pt2Si and PtSi

We have carried out a first-principles study of the elastic properties and electronic structure for two roomtemperature stable Pt silicide phases, tetragonal a-Pt2Si, and orthorhombic PtSi. We have calculated all of the equilibrium structural parameters for both phases: the a and c lattice constants for a-Pt2Si and the a, b, and c lattice constants and four internal structural parameters for PtSi. These results agree closely with experimental data. We have also calculated the zero-pressure elastic constants, confirming prior results for pure Pt and Si and predicting values for the six ~nine! independent, nonzero elastic constants of a-Pt2Si ~PtSi!. These calculations include a full treatment of all relevant internal displacements induced by the elastic strains, including an explicit determination of the dimensionless internal displacement parameters for the three strains in a-Pt2Si for which they are nonzero. We have analyzed the trends in the calculated elastic constants, both within each material as well as among the two silicides and the pure Pt and Si phases. The calculated electronic structure confirms that the two silicides are poor metals with a low density of states at the Fermi level, and consequently we expect that the Drude component of the optical absorption will be much smaller than in good metals such as pure Pt. This observation, combined with the topology found in the first-principles spin-orbit split band structure, suggests that it may be important to include the interband contribution to the optical absorption, even in the infrared region.

AFLOW: An Automatic Framework for High-throughput Materials Discovery

Abstract Recent advances in computational materials science present novel opportunities for structure discovery and optimization, including uncovering of unsuspected compounds and metastable structures, electronic structure, surface, and nano-particle properties. The practical realization of these opportunities requires systematic generation and classification of the relevant computational data by high-throughput methods. In this paper we present A flow (Automatic Flow), a software framework for high-throughput calculation of crystal structure properties of alloys, intermetallics and inorganic compounds. The A flow software is available for the scientific community on the website of the materials research consortium, aflowlib.org. Its geometric and electronic structure analysis and manipulation tools are additionally available for online operation at the same website. The combination of automatic methods and user online interfaces provide a powerful tool for efficient quantum computational materials discovery and characterization.

Algorithm for Generating Derivative Structures

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. The run time of the algorithm scales linearly with the number of unique structures found. We show several applications demonstrating how the algorithm can be used in materials design problems. We predict an altogether new crystal structure in Cd-Pt and Pd-Pt, and several new ground states in Pd-rich and Pt-rich binary systems.

Compressive sensing as a paradigm for building physics models

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 learning algorithms (genetic programming, neural networks, Bayesian methods, etc.) attempt to eliminate the a priori need for such intuition but often do so with increased computational burden and human time. A recently-developed technique in the field of signal processing, compressive sensing (CS), provides a simple, general, and efficient way of finding the key descriptive variables. CS is a new paradigm for model building-we show that its models are just as robust as those built by current state-of-the-art approaches, but can be constructed at a fraction of the computational cost and user effort.

UNCLE: a code for constructing cluster expansions for arbitrary lattices with minimal user-input

We present a new implementation of the cluster expansion formalism. The new code, UNiversal CLuster Expansion (UNCLE), consolidates recent advances in the methodology and leverages one new development in the formalism itself. As a core goal, the package reduces the need for user intervention, automating the method to reduce human error and judgment. The package extends standard cluster expansion formalism to the more complicated cases of ternary compounds, as well as surfaces, including adsorption and inequivalent sites.

Uncovering compounds by synergy of cluster expansion and high-throughput methods.

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 algorithms. Evolutionary algorithms have been recently introduced to search for stable off-lattice structures at fixed mixture compositions. The general off-lattice problem is still unsolved. We present an integrated approach of CE and high-throughput ab initio calculations (HT) applicable to the full range of compositions in binary systems where the constituent elements or the intermediate ordered structures have different lattice types. The HT method replaces the search algorithms by direct calculation of a moderate number of naturally occurring prototypes representing all crystal systems and guides CE calculations of derivative structures. This synergy achieves the precision of the CE and the guiding strengths of the HT. Its application to poorly characterized binary Hf systems, believed to be phase-separating, defines three classes of alloys where CE and HT complement each other to uncover new ordered structures.

Ordered Structures in Rhenium Binary Alloys from First-Principles Calculations

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 high-throughput first-principles calculations predict stable ordered structures in 20 of those 28 systems. In the known compound-forming systems, they reproduce all the known compounds and predict a few unreported ones. These results indicate the need for an extensive revision of our current understanding of Re alloys through a combination of theoretical predictions and experimental validations. The following systems are investigated: AgRe(★), AuRe(★), CdRe(★), CoRe, CrRe(★), CuRe(★), FeRe, HfRe, HgRe(★), IrRe, MnRe, MoRe, NbRe, NiRe, OsRe, PdRe, PtRe, ReRh, ReRu, ReSc, ReTa, ReTc, ReTi, ReV, ReW(★), ReY, ReZn(★), and ReZr ((★) = systems in which the ab initio method predicts that no compounds are stable).

Phonon and elastic instabilities in MoC and MoN

We present several results related to the instability of MoC and MoN in the B1 ~sodium chloride! structure. These compounds were proposed as potential superconductors with moderately high transition temperatures. We show that the elastic instability in B1-structure MoN, demonstrated several years ago, persists at elevated pressures, thus offering little hope of stabilizing this material without chemical doping. For MoC, another material for which stoichiometric fabrication in the B1 structure has not proven possible, we find that all of the cubic elastic constants are positive, indicating elastic stability. Instead, we find X-point phonon instabilities in MoC ~and in MoN as well!, further illustrating the rich behavior of carbo-nitride materials. The early transition metal carbides and nitrides represent a technologically important series of materials, often revealing an interplay between their interesting properties and the incipient instabilities that seem to drive those properties. 1 The important features of these materials include extreme hardness and high melting temperatures, as well as superconductivity in many cases. In some of these materials, the atomistic properties ~e.g., bonding properties! that drive particular macroscopic behaviors can also lead to instabilities that inhibit the stoichiometric B1 ~sodium chloride! structure from forming. MoC and MoN are good examples of this circumstance. In this paper, we report theoretical results related to the stability of B1 MoC and MoN. We show that the elastic instability in MoN is not mitigated, but rather enhanced, by the application of pressure, and that the difficulty in fabricating B1 MoC is due to a phonon instability at the X point in the Brillouin zone ~BZ!. All the calculations reported here were performed with the linear-augmented

The New Face of Rhodium Alloys: Revealing Ordered Structures from First Principles

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 calculations predict stable ordered structures in 9 of those 14 binary systems. They also predict a few unreported compounds in the known compound-forming systems. These results indicate the need for an extensive revision of our current understanding of Rh alloys through a combination of theoretical predictions and experimental validations.