Axel van de Walle

Bio: Axel van de Walle is an academic researcher from Brown University. The author has contributed to research in topics: Phase diagram & Cluster expansion. The author has an hindex of 24, co-authored 83 publications receiving 1935 citations. Previous affiliations of Axel van de Walle include California Institute of Technology & Northwestern University.

Multicomponent multisublattice alloys, nonconfigurational entropy and other additions to the Alloy Theoretic Automated Toolkit

Abstract: A number of new functionalities have been added to the Alloy Theoretic Automated Toolkit (ATAT) since it was last reviewed in this journal in 2002. ATAT can now handle multicomponent multisublattice alloy systems, nonconfigurational sources of entropy (e.g. vibrational and electronic entropy), Special Quasirandom Structures (SQS) generation, tensorial cluster expansion construction and includes interfaces for multiple atomistic or ab initio codes. This paper presents an overview of these features geared towards the practical use of the code. The extensions to the cluster expansion formalism needed to cover multicomponent multisublattice alloys are also formally demonstrated.

Theoretical prediction of high melting temperature for a Mo–Ru–Ta–W HCP multiprincipal element alloy

Abstract: While rhenium is an ideal material for rapid thermal cycling applications under high temperatures, such as rocket engine nozzles, its high cost limits its widespread use and prompts an exploration of viable cost-effective substitutes. In prior work, we identified a promising pool of candidate substitute alloys consisting of Mo, Ru, Ta, and W. In this work we demonstrate, based on density functional theory melting temperature calculations, that one of the candidates, Mo0.292Ru0.555Ta0.031W0.122, exhibits a high melting temperature (around 2626 K), thus supporting its use in high-temperature applications.

Thermodynamic properties of binary hcp solution phases from special quasirandom structures

Abstract: Three different special quasirandom structures (SQS's) of the substitutional hcp A1–xBx binary random solutions (x=0.25, 0.5, and 0.75) are presented. These structures are able to mimic the most important pair and multi-site correlation functions corresponding to perfectly random hcp solutions at those compositions. Due to the relatively small size of the generated structures, they can be used to calculate the properties of random hcp alloys via first-principles methods. The structures are relaxed in order to find their lowest energy configurations at each composition. In some cases, it was found that full relaxation resulted in complete loss of their parental symmetry as hcp so geometry optimizations in which no local relaxations are allowed were also performed. In general, the first-principles results for the seven binary systems (Cd-Mg, Mg-Zr, Al-Mg, Mo-Ru, Hf-Ti, Hf-Zr, and Ti-Zr) show good agreement with both formation enthalpy and lattice parameters measurements from experiments. It is concluded that the SQS's presented in this work can be widely used to study the behavior of random hcp solutions.

Prediction of the material with highest known melting point from ab initio molecular dynamics calculations

Abstract: Using electronic structure calculations, we conduct an extensive investigation into the Hf-Ta-C system, which includes the compounds that have the highest melting points known to date. We identify three major chemical factors that contribute to the high melting temperatures. Based on these factors, we propose a class of materials that may possess even higher melting temperatures and explore it via efficient ab initio molecular dynamics calculations in order to identify the composition maximizing the melting point. This study demonstrates the feasibility of automated and high-throughput materials screening and discovery via ab initio calculations for the optimization of “higher-level” properties, such as melting points, whose determination requires extensive sampling of atomic configuration space.

Defect-controlled electronic properties in AZn2Sb2 Zintl phases

Abstract: Experimentally, AZn_2Sb_2 samples (A=Ca, Sr, Eu, Yb) are found to have large charge carrier concentrations that increase with increasing electronegativity of A. Using density functional theory (DFT) calculations, we show that this trend can be explained by stable cation vacancies and the corresponding finite phase width in A1−xZn_2Sb_2 compounds.

Fast parallel algorithms for short-range molecular dynamics

Abstract: Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

Machine learning in materials informatics: recent applications and prospects

Abstract: Propelled partly by the Materials Genome Initiative, and partly by the algorithmic developments and the resounding successes of data-driven efforts in other domains, informatics strategies are beginning to take shape within materials science. These approaches lead to surrogate machine learning models that enable rapid predictions based purely on past data rather than by direct experimentation or by computations/simulations in which fundamental equations are explicitly solved. Data-centric informatics methods are becoming useful to determine material properties that are hard to measure or compute using traditional methods—due to the cost, time or effort involved—but for which reliable data either already exists or can be generated for at least a subset of the critical cases. Predictions are typically interpolative, involving fingerprinting a material numerically first, and then following a mapping (established via a learning algorithm) between the fingerprint and the property of interest. Fingerprints, also referred to as “descriptors”, may be of many types and scales, as dictated by the application domain and needs. Predictions may also be extrapolative—extending into new materials spaces—provided prediction uncertainties are properly taken into account. This article attempts to provide an overview of some of the recent successful data-driven “materials informatics” strategies undertaken in the last decade, with particular emphasis on the fingerprint or descriptor choices. The review also identifies some challenges the community is facing and those that should be overcome in the near future.

Efficient stochastic generation of special quasirandom structures

Abstract: We present a new algorithm to generate Special Quasirandom Structures (SQS), i.e., best periodic supercell approximations to the true disordered state for a given number of atoms per supercell. The method is based on a Monte Carlo simulated annealing loop with an objective function that seeks to perfectly match the maximum number of correlation functions (as opposed to merely minimizing the distance between the SQS correlation and the disordered state correlations for a pre-specified set of correlations). The proposed method optimizes the shape of the supercell jointly with the occupation of the atomic sites, thus ensuring that the configurational space searched is exhaustive and not biased by a pre-specified supercell shape. The method has been implemented in the “mcsqs” code of the Alloy Theoretic Automated Toolkit (ATAT) in the most general framework of multicomponent multisublattice systems and in a way that minimizes the amount of input information the user needs to specify and that allows for efficient parallelization.

High-entropy ceramics

Abstract: Disordered multicomponent systems, occupying the mostly uncharted centres of phase diagrams, were proposed in 2004 as innovative materials with promising applications. The idea was to maximize the configurational entropy to stabilize (near) equimolar mixtures and achieve more robust systems, which became known as high-entropy materials. Initial research focused mainly on metal alloys and nitride films. In 2015, entropy stabilization was demonstrated in a mixture of oxides. Other high-entropy disordered ceramics rapidly followed, stimulating the addition of more components to obtain materials expressing a blend of properties, often highly enhanced. The systems were soon proven to be useful in wide-ranging technologies, including thermal barrier coatings, thermoelectrics, catalysts, batteries and wear-resistant and corrosion-resistant coatings. In this Review, we discuss the current state of the disordered ceramics field by examining the applications and the high-entropy features fuelling them, covering both theoretical predictions and experimental results. The influence of entropy is unavoidable and can no longer be ignored. In the space of ceramics, it leads to new materials that, both as bulk and thin films, will play important roles in technology in the decades to come. The valuable combination of disorder and non-metallic bonding gives rise to high-entropy ceramics. This Review explores the structures and chemistries of these versatile materials, and their applications in catalysis, water splitting, energy storage, thermoelectricity and thermal, environmental and wear protection.

QuantumATK: an integrated platform of electronic and atomic-scale modelling tools.

Abstract: QuantumATK is an integrated set of atomic-scale modelling tools developed since 2003 by professional software engineers in collaboration with academic researchers. While different aspects and individual modules of the platform have been previously presented,a#13; the purpose of this paper is to give a general overview of the platform. The QuantumATK simulation engines enable electronic-structure calculations using density functional theory or tight-binding model Hamiltonians, and also offers bonded or reactive empirical force fields in many different parametrizations. Density functional theory is implemented using either a plane-wave basis or expansion of electronic states in a linear combination of atomic orbitals. The platform includes a long list of advanced modules, including Green's-function methods for electron transport simulations and surface calculations, first-principles electron-phonon and electron-photon couplings,a#13; simulation of atomic-scale heat transport, ion dynamics, spintronics, optical properties of materials, static polarization, and more.a#13; Seamless integration of the different simulation engines into a common platform allows for easy combination of different simulation methods into complex workflows. Besides giving a general overview and presenting a number of implementation detailsa#13; not previously published, we also present four different application examples. These are calculations of the phonon-limited mobility of Cu, Ag and Au, electron transport in a gated 2D device, multi-model simulation of lithium ion drift through a battery cathode in an external electric field, and electronic-structure calculations of the composition-dependent band gap of SiGe alloys.a#13;