Two-Layer High-Throughput:Effective Mass Calculations Including Warping

In this paper,we perform two-layer high-throughput calculations.In the first layer,which involves changing the crystal structure and/or chemical composition,we analyze selected Ⅲ-Ⅴ semiconductors,filled and unfilled skutterudites,as well as rock salt and layered chalcogenides.The second layer searches the full Brillouin zone(BZ)for critical points within 1.5 eV(1 eV=1.602176×10^(-19)J)of the Fermi level and characterizes those points by computing the effective masses.We introduce several methods to compute the effective masses from first principles and compare them to each other.Our approach also includes the calculation of the density-of-states effective masses for warped critical points,where traditional approaches fail to give consistent results due to an underlying non-analytic behavior of the critical point.We demonstrate the need to consider the band structure in its full complexity and the value of complementary approaches to compute the effective masses.We also provide computational evidence that warping occurs only in the presence of degeneracies.

Thermodynamic analysis of metal segregation in dual phase high entropy ceramics

Equilibrium Gibbs'free energy calculations were used to determine metal segregation trends between boride and carbide solid solutions containing two metals that are relevant to dual phase high entropy ceramics.The model predicted that Ti had the strongest tendency to segregate to the boride phase followed by Zr,Nb,Mo,V,Hf,and Ta,which matches experimental results of measured compositions.The ratio of a metal in the carbide phase to the content of the same metal in the corresponding metal boride had a linear trend with the change in standard Gibbs'free energy of reaction for a metal carbide reacting with B_(4)C to produce its corresponding metal boride and carbon.The proposed model was used to predict the changes in standard Gibbs'free energy for CrC→CrB_(2) to be−260 kJ and WC→WB_(2) to be 148 kJ,which indicates that Cr has the strongest segregation to the boride and W has the strongest segregation to the carbide.The proposed model can be used to estimate the segregation of metals in dual phase high entropy boride-carbide ceramics of any boride/carbide ratio or metal content.

Machine learning modeling of superconducting critical temperature

Superconductivity has been the focus of enormous research effort since its discovery more than a century ago.Yet,some features of this unique phenomenon remain poorly understood;prime among these is the connection between superconductivity and chemical/structural properties of materials.To bridge the gap,several machine learning schemes are developed herein to model the critical temperatures(T_(c))of the 12,000+known superconductors available via the SuperCon database.Materials are first divided into two classes based on their T_(c) values,above and below 10 K,and a classification model predicting this label is trained.The model uses coarse-grained features based only on the chemical compositions.It shows strong predictive power,with out-of-sample accuracy of about 92%.Separate regression models are developed to predict the values of T_(c) for cuprate,iron-based,and low-T_(c) compounds.These models also demonstrate good performance,with learned predictors offering potential insights into the mechanisms behind superconductivity in different families of materials.To improve the accuracy and interpretability of these models,new features are incorporated using materials data from the AFLOW Online Repositories.Finally,the classification and regression models are combined into a single-integrated pipeline and employed to search the entire Inorganic Crystallographic Structure Database(ICSD)for potential new superconductors.We identify>30 non-cuprate and non-iron-based oxides as candidate materials.

Discovery of high-entropy ceramics via machine learning

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 methods rely on the availability of sufficient experimental data and computational power.Machine learning(ML)applied to materials science can accelerate development and reduce costs.In this study,we propose an ML method,leveraging thermodynamic and compositional attributes of a given material for predicting the synthesizability(i.e.,entropy-forming ability)of disordered metal carbides.

Predicting superhard materials via a machine learning informed evolutionary structure search

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 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 materials within the AFLOW repository.Because H^(ML)_(v) values can be quickly estimated,they can be used in conjunction with an evolutionary search to predict stable,superhard materials.This methodology is implemented in the XTALOPT evolutionary algorithm.Each crystal is minimized to the nearest local minimum,and its Vickers hardness is computed via a linear relationship with the shear modulus discovered by Teter.Both the energy/enthalpy and H^(ML)_(v),Teter are employed to determine a structure’s fitness.This implementation is applied towards the carbon system,and 43 new superhard phases are found.A topological analysis reveals that phases estimated to be slightly harder than diamond contain a substantial fraction of diamond and/or lonsdaleite.

AFLOW-XtalFinder:a reliable choice to identify crystalline prototypes

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 internal degrees of freedom consistent with the International Tables for Crystallography.To ensure uniqueness,structures are analyzed and compared via symmetry,local atomic geometries,and crystal mapping techniques,simultaneously grouping them by similarity.The software(i)distinguishes distinct crystal prototypes and atom decorations,(ii)determines equivalent spin configurations,(iii)reveals compounds with similar properties,and(iv)guides the discovery of unexplored materials.The operations are accessible through a Python module ready for workflows,and through command line syntax.All the 4+million compounds in the AFLOW.org repositories are mapped to their ideal prototype,allowing users to search database entries via symbolic structure-type.Furthermore,15,000 unique structures—sorted by prevalence—are extracted from the AFLOW-ICSD catalog to serve as future prototypes in the Encyclopedia.

An efficient and accurate framework for calculating lattice thermal conductivity of solids:AFLOW-AAPL Automatic Anharmonic Phonon Library

One of the most accurate approaches for calculating lattice thermal conductivity,κ_(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 lack of automation in the frameworks using this methodology,which affect the discovery rate of novel materials with ad-hoc properties.Here,the Automatic Anharmonic Phonon Library(AAPL)is presented.It efficiently computes interatomic force constants by making effective use of crystal symmetry analysis,it solves the Boltzmann transport equation to obtain κ_(l),and allows a fully integrated operation with minimum user intervention,a rational addition to the current high-throughput accelerated materials development framework AFLOW.An“experiment vs.theory”study of the approach is shown,comparing accuracy and speed with respect to other available packages,and for materials characterized by strong electron localization and correlation.Combining AAPL with the pseudo-hybrid functional ACBN0 is possible to improve accuracy without increasing computational requirements.

Unavoidable disorder and entropy in multi-component systems

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 the AFLOW data repositories.A threshold in the number of species is found where entropy gain exceeds enthalpy gain.Beyond that,enthalpy can be neglected,and disorder—complete or partial—is unavoidable.

Coordination corrected ab initio formation enthalpies

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,and also capable of correcting relative stability of polymorphs.CCE uses calculations employing the Perdew,Burke and Ernzerhof(PBE),local density approximation(LDA)and strongly constrained and appropriately normed(SCAN)exchange correlation functionals,in conjunction with a quasiharmonic Debye model to treat zero-point vibrational and thermal effects.The benchmark,performed on binary and ternary oxides(halides),shows very accurate room temperature results for all functionals,with the smallest mean absolute error of 27(24)meV/atom obtained with SCAN.The zero-point vibrational and thermal contributions to the formation enthalpies are small and with different signs—largely canceling each other.

Parametrically constrained geometry relaxations for high-throughput materials science

Reducing parameter spaces via exploiting symmetries has greatly accelerated and increased the quality of electronic-structure calculations.Unfortunately,many of the traditional methods fail when the global crystal symmetry is broken,even when the distortion is only a slight perturbation(e.g.,Jahn-Teller like distortions).Here we introduce a flexible and generalizable parametric relaxation scheme and implement it in the all-electron code FHI-aims.This approach utilizes parametric constraints to maintain symmetry at any level.After demonstrating the method’s ability to relax metastable structures,we highlight its adaptability and performance over a test set of 359 materials,across 13 lattice prototypes.Finally we show how these constraints can reduce the number of steps needed to relax local lattice distortions by an order of magnitude.The flexibility of these constraints enables a significant acceleration of high-throughput searches for novel materials for numerous applications.

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

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,Mo_(0.292)Ru_(0.555)Ta_(0.031)W_(0.122),exhibits a high melting temperature(around 2626 K),thus supporting its use in high-temperature applications.

Spin Hall effect in prototype Rashba ferroelectrics GeTe and SnTe

Ferroelectric Rashba semiconductors(FERSCs)have recently emerged as a promising class of spintronics materials.The peculiar coupling between spin and polar degrees of freedom responsible for several exceptional properties,including ferroelectric switching of Rashba spin texture,suggests that the electron’s spin could be controlled by using only electric fields.In this regard,recent experimental studies revealing charge-to-spin interconversion phenomena in two prototypical FERSCs,GeTe and SnTe,appear extremely relevant.Here,by employing density functional theory calculations,we investigate spin Hall effect(SHE)in these materials and show that it can be large either in ferroelectric or paraelectric structure.We further explore the compatibility between doping required for the practical realization of SHE in semiconductors and polar distortions which determine Rashba-related phenomena in FERSCs,but which could be suppressed by free charge carriers.