# Cormac Toher's research while affiliated with University of Texas at Dallas and other places

## Publications (81)

Article
The realization of novel technological opportunities given by computational and autonomous materials design requires efficient and effective frameworks. For more than two decades, aflow++ (Automatic-Flow Framework for Materials Discovery) has provided an interconnected collection of algorithms and workflows to address this challenge. This article c...
Article
High entropy oxides are emerging as an exciting new avenue to design highly tailored functional behaviors that have no traditional counterparts. Study and application of these materials are bringing together scientists and engineers from physics, chemistry, and materials science. The diversity of each of these disciplines comes with perspectives an...
Article
To enable materials databases supporting computational and experimental research, it is critical to develop platforms that both facilitate access to the data and provide the tools used to generate/analyze it-all while considering the diversity of users' experience levels and usage needs. The recently formulated FAIR principles (Findable, Accessible...
Article
Full-text available
Discovering multifunctional materials with tunable plasmonic properties, capable of surviving harsh environments is critical for advanced optical and telecommunication applications. We chose high-entropy transition-metal carbides because of their exceptional thermal, chemical stability, and mechanical properties. By integrating computational thermo...
Preprint
Full-text available
High entropy oxides are emerging as an exciting new avenue to design highly tailored functional behaviors that have no traditional counterparts. Study and application of these materials are bringing together scientists and engineers from physics, chemistry, and materials science. This diversity of disciplines each come with perspectives and jargon...
Preprint
Full-text available
To enable materials databases supporting computational and experimental research, it is critical to develop platforms that both facilitate access to the data and provide the tools used to generate/analyze it - all while considering the diversity of users' experience levels and usage needs. The recently formulated FAIR principles (Findable, Accessib...
Article
A metallic, covalently bonded carbon allotrope is predicted via first principles calculations. It is composed of an sp 3 carbon framework that acts as a diamond anvil cell by constraining the distance between parallel cis‐polyacetylene chains. The distance between these sp 2 carbon atoms renders the phase metallic, and yields two well‐nested nearly...
Article
A metallic, covalently bonded carbon allotrope is predicted via first principles calculations. It is composed of an sp 3 carbon framework that acts as a diamond anvil cell by constraining the distance between parallel cis‐polyacetylene chains. The distance between these sp 2 carbon atoms renders the phase metallic, and yields two well‐nested nearly...
Article
Disorder enhances desired properties, as well as creating new avenues for synthesizing materials. For instance, hardness and yield stress are improved by solid-solution strengthening, a result of distortions and atomic-size mismatches. Thermochemical stability is increased by the preference of chemically disordered mixtures for high-symmetry superl...
Preprint
Full-text available
A metallic covalently bonded carbon allotrope is predicted via first principles calculations. It is composed of an $sp^3$ carbon framework that acts as a diamond anvil cell by constraining the distance between parallel cis-polyacetylene chains. The distance between these $sp^2$ carbon atoms renders the phase metallic, and yields two well-nested nea...
Article
Full-text available
In recent years, we have been witnessing a paradigm shift in computational materials science. In fact, traditional methods, mostly developed in the second half of the XXth century, are being complemented, extended, and sometimes even completely replaced by faster, simpler, and often more accurate approaches. The new approaches, that we collectively...
Article
Application of artificial intelligence (AI), and more specifically machine learning, to the physical sciences has expanded significantly over the past decades. In particular, science-informed AI, also known as scientific AI or inductive bias AI, has grown from a focus on data analysis to now controlling experiment design, simulation, execution and...
Article
Full-text available
The accelerated growth rate of repository entries in crystallographic databases makes it arduous to identify and classify their prototype structures. The open-source AFLOW-XtalFinder package was developed to solve this problem. It symbolically maps structures into standard designations following the AFLOW Prototype Encyclopedia and calculates the i...
Preprint
Full-text available
Disorder enhances desired properties, as well as creating new avenues for synthesizing materials. For instance, hardness and yield stress are improved by solid-solution strengthening, a result of distortions and atomic size mismatches. Thermo-chemical stability is increased by the preference of chemically disordered mixtures for high-symmetry super...
Preprint
Application of artificial intelligence (AI), and more specifically machine learning, to the physical sciences has expanded significantly over the past decades. In particular, science-informed AI or scientific AI has grown from a focus on data analysis to now controlling experiment design, simulation, execution and analysis in closed-loop autonomous...
Article
The AFLOW Library of Crystallographic Prototypes has been extended to include a total of 1,100 common crystal structural prototypes (510 new ones with Part 3), comprising all of the inorganic crystal structures defined in the seven-volume Strukturbericht series published in Germany from 1937 through 1943. We cover a history of the Strukturbericht d...
Article
Full-text available
The entropy landscape of high‐entropy carbides can be used to understand and predict their structure, properties, and stability. Using first principles calculations, the individual and temperature‐dependent contributions of vibrational, electronic, and configurational entropies are analyzed, and compare them qualitatively to the enthalpies of mixin...
Article
Full-text available
High-entropy ceramics are attracting significant interest due to their exceptional chemical stability and physical properties. While configurational entropy descriptors have been successfully implemented to predict their formation and even to discover new materials, the contribution of vibrations to their stability has been contentious. This work u...
Article
Full-text available
The Open Databases Integration for Materials Design (OPTIMADE) consortium has designed a universal application programming interface (API) to make materials databases accessible and interoperable. We outline the first stable release of the specification, v1.0, which is already supported by many leading databases and several software packages. We il...
Article
Full-text available
Alloy modelling has a history of machine-learning-like approaches, preceding the tide of data-science-inspired work. The dawn of computational databases has made the integration of analysis, prediction and discovery the key theme in accelerated alloy research. Advances in machine-learning methods and enhanced data generation have created a fertile...
Article
The computational design of materials with ionic bonds poses a critical challenge to thermodynamic modeling since density functional theory yields inaccurate predictions of their formation enthalpies. Progress requires leveraging physically insightful correction methods. The recently introduced coordination corrected enthalpies (CCE) method deliver...
Preprint
The Open Databases Integration for Materials Design (OPTIMADE) consortium has designed a universal application programming interface (API) to make materials databases accessible and interoperable. We outline the first stable release of the specification, v1.0, which is already supported by many leading databases and several software packages. We il...
Preprint
The entropy landscape of high entropy carbides-chemically disordered ceramics with five different transition metal elements populating the cation sites of the rock salt carbide lattice-can be used to understand and predict their structure, properties, and stability. Using first principles calculations, we analyze the individual and temperature-depe...
Preprint
The AFLOW Library of Crystallographic Prototypes has been extended to include a total of 1,100 common crystal structural prototypes (510 new ones with Part 3), comprising all of the inorganic crystal structures defined in the seven-volume Strukturbericht series published in Germany from 1937 through 1943. We cover a history of the Strukturbericht d...
Article
Full-text available
Although high-entropy materials are attracting considerable interest due to a combination of useful properties and promising applications, predicting their formation remains a hindrance for rational discovery of new systems. Experimental approaches are based on physical intuition and/or expensive trial and error strategies. Most computational metho...
Article
Full-text available
Active learning—the field of machine learning (ML) dedicated to optimal experiment design—has played a part in science as far back as the 18th century when Laplace used it to guide his discovery of celestial mechanics. In this work, we focus a closed-loop, active learning-driven autonomous system on another major challenge, the discovery of advance...
Preprint
The accelerated growth rate of repository entries in crystallographic databases makes it arduous to identify and classify their prototype structures. The open-source AFLOW-XtalFinder package was developed to solve this problem. It symbolically maps structures into standard designations following the AFLOW Prototype Encyclopedia and calculates the i...
Preprint
Full-text available
Active learning - the field of machine learning (ML) dedicated to optimal experiment design, has played a part in science as far back as the 18th century when Laplace used it to guide his discovery of celestial mechanics [1]. In this work we focus a closed-loop, active learning-driven autonomous system on another major challenge, the discovery of a...
Article
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 res...
Article
Full-text available
The computational prediction of superhard materials would enable the in silico design of compounds that could be used in a wide variety of technological applications. Herein, good agreement was found between experimental Vickers hardnesses, Hv, of a wide range of materials and those calculated by three macroscopic hardness models that employ the sh...
Article
Full-text available
The need for improved functionalities is driving the search for more complicated multi-component materials. Despite the factorially increasing composition space, ordered compounds with four or more species are rare. Here, we unveil the competition between the gain in enthalpy and entropy with increasing number of species by statistical analysis of...
Article
Accelerating the calculations of finite-temperature thermodynamic properties is a major challenge for rational materials design. Reliable methods can be quite expensive, limiting their applicability in autonomous high-throughput workflows. Here, the three-phonon quasiharmonic approximation (QHA) method is introduced, requiring only three phonon cal...
Article
Metallic glasses are excellent candidates for biomedical implant applications due to their inherent strength and corrosion resistance. However, use of metallic glasses in structural applications is limited because bulk dimensions are challenging to achieve. Glass-forming ability (GFA) varies strongly with alloy composition and becomes more difficul...
Preprint
Good agreement was found between experimental Vickers hardnesses, $H_\text{v}$, of a wide range of materials and those calculated by three macroscopic hardness models that employ the shear and/or bulk moduli obtained from: (i) first principles via AFLOW-AEL (AFLOW Automatic Elastic Library), and (ii) a machine learning (ML) model trained on materia...
Preprint
The need for improved functionalities is driving the search for more complicated multi-component materials. Despite the factorially increasing composition space, ordered compounds with 4 or more species are rare. Here, we unveil the competition between the gain in enthalpy and entropy with increasing number of species by statistical analysis of the...
Article
Full-text available
The correct calculation of formation enthalpy is one of the enablers of ab-initio computational materials design. For several classes of systems (e.g. oxides) standard density functional theory produces incorrect values. Here we propose the “coordination corrected enthalpies” method (CCE), based on the number of nearest neighbor cation–anion bonds,...
Article
Materials discovery via high-throughput methods relies on the availability of structural prototypes, which are generally decorated with varying combinations of elements to produce potential new materials. To facilitate the automatic generation of these materials, we developed The AFLOW Library of Crystallographic Prototypes — a collection of crysta...
Preprint
Metallic glasses are excellent candidates for biomedical implant applications due to their inherent strength and corrosion resistance. Use of metallic glasses in structural applications is limited, however, because bulk dimensions are challenging to achieve. Glass-forming ability (GFA) varies strongly with alloy composition and becomes more difficu...
Article
Version 12 of XtalOpt, an evolutionary algorithm for crystal structure prediction, is now available for download from the CPC program library or the XtalOpt website, http://xtalopt.github.io. The new version includes: a method for calculating hardness using a machine learning algorithm within AFLOW-ML (Automatic FLOW for Materials Discovery — Machi...
Article
Twelve different equiatomic five-metal carbides of group IVB, VB, and VIB refractory transition metals are synthesized via high-energy ball milling and spark plasma sintering. Implementation of a newly developed ab initio entropy descriptor aids in selection of candidate compositions for synthesis of high entropy and entropy stabilized carbides. Ph...
Article
Full-text available
High-entropy materials have attracted considerable interest due to the combination of useful properties and promising applications. Predicting their formation remains the major hindrance to the discovery of new systems. Here we propose a descriptor—entropy forming ability—for addressing synthesizability from first principles. The formalism, based o...
Preprint
The correct calculation of formation enthalpy is one of the enablers of ab-initio computational materials design. For several classes of systems (e.g. oxides) standard density functional theory produces incorrect values. Here we propose the "coordination-corrected-enthalpies" method (CCE), based on the number of nearest neighbor cation-anion bonds,...
Preprint
Full-text available
High-entropy materials have attracted considerable interest due to the combination of useful properties and promising applications. Predicting their formation remains the major hindrance to the discovery of new systems. Here we propose a descriptor - entropy forming ability - for addressing synthesizability from first principles. The formalism, bas...
Article
A priori prediction of phase stability of materials is a challenging practice, requiring knowledge of all energetically-competing structures at formation conditions. Large materials repositories - housing properties of both experimental and hypothetical compounds - offer a path to prediction through the construction of informatics-based, ab-initio...
Article
The expansion of programmatically accessible materials data has cultivated opportunities for data-driven approaches. Workflows such as the Automatic Flow Framework for Materials Discovery not only manage the generation, storage, and dissemination of materials data, but also leverage the information for thermodynamic formability modeling, such as th...
Preprint
Full-text available
Accelerating the calculations of finite-temperature thermodynamic properties is a major challenge for rational materials design. Reliable methods can be quite expensive, limiting their effective applicability in autonomous high-throughput workflows. Here, the 3-phonons quasi-harmonic approximation (QHA) method is introduced, requiring only three ph...
Preprint
Full-text available
Materials discovery via high-throughput methods relies on the availability of structural prototypes, which are generally decorated with varying combinations of elements to produce potential new materials. To facilitate the automatic generation of these materials, we developed $\textit{The AFLOW Library of Crystallographic Prototypes}$ $\unicode{x20... Preprint \textit{A priori}$ prediction of phase stability of materials is a challenging practice, requiring knowledge of all energetically-competing structures at formation conditions. Large materials repositories $\unicode{x2014}$ housing properties of both experimental and hypothetical compounds $\unicode{x2014}$ offer a path to prediction through the con...
Preprint
Materials informatics offers a promising pathway towards rational materials design, replacing the current trial-and-error approach and accelerating the development of new functional materials. Through the use of sophisticated data analysis techniques, underlying property trends can be identified, facilitating the formulation of new design rules. Su...
Article
Full-text available
Spinodal decomposition is proposed for stabilizing self-assembled interfaces between topological insulators (TIs) by combining layers of iso-structural and iso-valent TlBiX2 (X=S, Se, Te) materials. The composition range for gapless states is addressed concurrently to the study of thermodynamically driven boundaries. By tailoring composition, the T...
Article
The expansion of programmatically-accessible materials data has cultivated opportunities for data-driven approaches. Highly-automated frameworks like AFLOW not only manage the generation, storage, and dissemination of materials data, but also leverage the information for thermodynamic formability modeling, such as the prediction of phase diagrams a...
Article
Determination of the symmetry profile of structures is a persistent challenge in materials science. Results often vary amongst standard packages, hindering autonomous materials development by requiring continuous user attention and educated guesses. Here, we present a robust procedure for evaluating the complete suite of symmetry properties, featur...
Article
Full-text available
One of the most accurate approaches for calculating lattice thermal conductivity, κ ℓ DMPSID=1, is solving the Boltzmann transport equation starting from third-order anharmonic force constants. In addition to the underlying approximations of ab-initio parameterization, two main challenges are associated with this path: high computational costs and...
Article
Full-text available
The traditional paradigm for materials discovery has been recently expanded to incorporate substantial data driven research. With the intent to accelerate the development and the deployment of new technologies, the AFLOW Fleet for computational materials design automates high-throughput first principles calculations, and provides tools for data ver...
Article
Full-text available
Machine learning approaches, enabled by the emergence of comprehensive databases of materials properties, are becoming a fruitful direction for materials analysis. As a result, a plethora of models have been constructed and trained on existing data to predict properties of new systems. These powerful methods allow researchers to target studies only...
Article
While the ongoing search to discover new high-entropy systems is slowly expanding beyond metals, a rational and effective method for predicting "$\textit{in silico}$" the solid solution forming ability of multi-component systems remains yet to be developed. In this article, we propose a novel high-throughput approach for estimating the transition t...
Article
The evaluation of lattice stabilities of unstable elemental phases is a long-standing problem in the computational assessment of phase diagrams. Here we tackle this problem by explicitly calculating phase diagrams of intermetallic systems where its effect should be most conspicuous, binary systems of titanium with bcc transition metals. Two types o...
Article
The recent comment by Sykora et al. [1] on the work of Nozaki et al. [2] pointed out that the phase shifts between two independent pathways through a meta-contacted benzene calculated in Figs. 4(f)-(g) of Ref. [2] include erroneous shifts, and that the phase shift should instead be . This statement in Ref. [1] is correct, and there was an error in...
Data
Supplementary Notes, Supplementary Tables and Supplementary References
Article
The well-known antiresonance around the middle of the HOMO–LUMO gap observed in the transmission spectra of the meta-contacted benzene molecular junctions is often explained as being caused by the destructive interference between electronic waves following two different pathways in real space around the phenyl ring. We show one contradictory scenar...
Article
Full-text available
We have investigated the crystallization kinetics of Cu50Zr50 metallic glass thin films using nanocalorimetry. The crystallization process is growth-controlled during heating and nucleation-controlled during cooling, resulting in different critical heating and cooling rates to suppress crystallization. Measurements over a wide range of scanning rat...
Article
Automated computational materials science frameworks rapidly generate large quantities of materials data useful for accelerated materials design. We have extended the data oriented AFLOW-repository API (Application-Program-Interface, as described in Comput. Mater. Sci. 93, 178 (2014)) to enable programmatic access to search queries. A URI-based sea...
Article
Thorough characterization of the thermo-mechanical properties of materials requires difficult and time-consuming experiments. This severely limits the availability of data and it is one of the main obstacles for the development of effective accelerated materials design strategies. The rapid screening of new potential systems requires highly integra...
Article
Full-text available
One of the most accurate approaches for calculating lattice thermal conductivity, $\kappa_l$, is solving the Boltzmann transport equation starting from third-order anharmonic force constants. In addition to the underlying approximations of ab-initio parameterization, two main challenges are associated with this path. High computational costs and la...
Article
Full-text available
The evaluation of phase stabilities of unstable elemental phases is a long-standing problem in the computational assessment of phase diagrams. Here we tackle this problem by explicitly calculating phase diagrams of intermetallic systems where its effect should be most conspicuous, binary systems of titanium with bcc transition metals (Mo, Nb, Ta an...
Data
Supplementary Figures 1-2