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Unavoidable disorder and entropy in multi-component systems

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npj Computational Materials 2019年第1期4卷 544-546页

作者： Cormac Toher Corey Oses David Hicks stefano curtarolo Department of Mechanical Engineering and Materials Science Duke UniversityDurhamNC 27708USA

The need for improved functionalities is driving the search for more complicated multi-component *** the factorially increasing composition space,ordered compounds with four or more species are ***,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 *** that,enthalpy can be neglected,and disorder—complete or partial—is unavoidable.

关键词： materials. entropy enthalpy

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

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npj Computational Materials 2021年第1期7卷 49-52页

作者： Qi-Jun Hong Jan Schroers Douglas Hofmann stefano curtarolo Mark Asta Axel van de Walle School of Engineering Brown UniversityProvidenceRI 02912USA Department of Mechanical Engineering and Materials Science Yale UniversityNew HavenCT 06511USA NASA Jet Propulsion Laboratory California Institute of Technology4800 Oak Grove Dr.PasadenaCA 91109USA Department of Mechanical Engineering and Materials Science and Center for Autonomous Materials Design Duke UniversityDurhamNC 27708USA Department of Materials Science and Engineering University of CaliforniaBerkeleyCA 94720USA Materials Sciences Division Lawrence Berkeley National LaboratoryBerkeleyCA 94720USA

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 *** prior work,we identified a promising pool of candidate substitute alloys consisting of Mo,Ru,Ta,and *** 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.

关键词： alloy cycling high

Spin Hall effect in prototype Rashba ferroelectrics GeTe and SnTe

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npj Computational Materials 2020年第1期6卷 1607-1613页

作者： Haihang Wang Priya Gopal Silvia Picozzi stefano curtarolo Marco Buongiorno Nardelli Jagoda Sławińska Department of Physics University of North TexasDentonTX 76203USA Consiglio Nazionale delle Ricerche Istituto SPINUOS L’AquilaSede di lavoro CNR-SPIN c/o Univ.G.D’Annunzio66100 ChietiItaly Center for Autonomous Materials Design Duke UniversityDurhamNC 27708USA Materials Science Electrical EngineeringPhysics and ChemistryDuke UniversityDurhamNC 27708USA

Ferroelectric Rashba semiconductors(FERSCs)have recently emerged as a promising class of spintronics *** 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 *** this regard,recent experimental studies revealing charge-to-spin interconversion phenomena in two prototypical FERSCs,GeTe and SnTe,appear extremely ***,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 *** 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.

关键词： structure Rashba ferroelectric

Machine learning modeling of superconducting critical temperature

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npj Computational Materials 2018年第1期3卷 405-418页

作者： Valentin Stanev Corey Oses A.Gilad Kusne Efrain Rodriguez Johnpierre Paglione stefano curtarolo Ichiro Takeuchi Department of Materials Science and Engineering University of MarylandCollege ParkMD 20742-4111USA Center for Nanophysics and Advanced Materials University of MarylandCollege ParkMD 20742USA Department of Mechanical Engineering and Materials Science Duke UniversityDurhamNC 27708USA Center for Materials Genomics Duke UniversityDurhamNC 27708USA National Institute of Standards and Technology GaithersburgMD 20899USA Department of Chemistry and Biochemistry University of MarylandCollege ParkMD 20742USA Department of Physics University of MarylandCollege ParkMD 20742USA Fritz-Haber-Institut der Max-Planck-Gesellschaft 14195 Berlin-DahlemGermany

Superconductivity has been the focus of enormous research effort since its discovery more than a century ***,some features of this unique phenomenon remain poorly understood;prime among these is the connection between superconductivity and chemical/structural properties of *** 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 *** 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 *** model uses coarse-grained features based only on the chemical *** 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) *** models also demonstrate good performance,with learned predictors offering potential insights into the mechanisms behind superconductivity in different families of *** improve the accuracy and interpretability of these models,new features are incorporated using materials data from the AFLOW Online ***,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 *** identify>30 non-cuprate and non-iron-based oxides as candidate materials.

关键词： learning critical offering

AFLOW-XtalFinder:a reliable choice to identify crystalline prototypes

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npj Computational Materials 2021年第1期7卷 264-283页

作者： David Hicks Cormac Toher Denise CFord Frisco Rose Carlo De Santo Ohad Levy Michael J.Mehl stefano curtarolo Department of Mechanical Engineering and Materials Science Duke UniversityDurhamNC 27708USA Center for Autonomous Materials Design Duke UniversityDurhamNC 27708USA Department of Physics NRCNP.O.Box 9001Beer-Sheva 84190Israel

The accelerated growth rate of repository entries in crystallographic databases makes it arduous to identify and classify their prototype *** open-source AFLOW-XtalFinder package was developed to solve this *** symbolically maps structures into standard designations following the AFLOW Prototype Encyclopedia and calculates the internal degrees of freedom consistent with the International Tables for *** ensure uniqueness,structures are analyzed and compared via symmetry,local atomic geometries,and crystal mapping techniques,simultaneously grouping them by *** 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 *** operations are accessible through a Python module ready for workflows,and through command line *** the 4+million compounds in the *** repositories are mapped to their ideal prototype,allowing users to search database entries via symbolic ***,15,000 unique structures—sorted by prevalence—are extracted from the AFLOW-ICSD catalog to serve as future prototypes in the Encyclopedia.

关键词： Python symbolic crystalline

Predicting superhard materials via a machine learning informed evolutionary structure search

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npj Computational Materials 2019年第1期5卷 352-362页

作者： Patrick Avery Xiaoyu Wang Corey Oses Eric Gossett Davide M.Proserpio Cormac Toher stefano curtarolo Eva Zurek Department of Chemistry State University of New York at BuffaloBuffaloNY 14260-3000USA Department of Mechanical Engineering and Materials Science Duke UniversityDurhamNC 27708USA Dipartimento di Chimica Universitàdegli Studi di MilanoVia Golgi 1920133 MilanoItaly Samara Center for Theoretical Materials Science(SCTMS) Samara State Technical UniversityMolodogvardeyskaya St.244443100 SamaraRussia Center for Autonomous Materials Design Department of Mechanical Engineering and Materials ScienceDuke UniversityDurhamNC 27708USA

The computational prediction of superhard materials would enable the in silico design of compounds that could be used in a wide variety of technological ***,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 *** H^(ML)_(v) values can be quickly estimated,they can be used in conjunction with an evolutionary search to predict stable,superhard *** methodology is implemented in the XTALOPT evolutionary *** crystal is minimized to the nearest local minimum,and its Vickers hardness is computed via a linear relationship with the shear modulus discovered by *** the energy/enthalpy and H^(ML)_(v),Teter are employed to determine a structure’s *** 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.

关键词： structure modulus Elastic

Coordination corrected ab initio formation enthalpies

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npj Computational Materials 2019年第1期5卷 633-644页

作者： Rico Friedrich Demet Usanmaz Corey Oses Andrew Supka Marco Fornari Marco Buongiorno Nardelli Cormac Toher stefano curtarolo Department of Mechanical Engineering and Materials Science Duke UniversityDurhamNC 27708USA Center for Materials Genomics Duke UniversityDurhamNC 27708USA Department of Physics and Science of Advanced Materials Program Central Michigan UniversityMount PleasantMI 48859USA Department of Physics and Department of Chemistry University of North TexasDentonTX 76203USA Materials Science Electrical EngineeringPhysics and ChemistryDuke UniversityDurhamNC 27708USA Fritz-Haber-Institut der Max-Planck-Gesellschaft 14195 Berlin-DahlemGermany

The correct calculation of formation enthalpy is one of the enablers of ab-initio computational materials *** several classes of systems(***)standard density functional theory produces incorrect *** 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 *** 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 *** 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 *** zero-point vibrational and thermal contributions to the formation enthalpies are small and with different signs—largely canceling each other.

关键词： corrected vibrational oxides

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

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npj Computational Materials 2017年第1期3卷 79-88页

作者： Jose J.Plata Pinku Nath Demet Usanmaz Jesus Carrete Cormac Toher Maarten de Jong Mark Asta Marco Fornari Marco Buongiorno Nardelli stefano curtarolo Department of Mechanical Engineering and Materials Science Duke UniversityDurhamNC 27708USA Institute of Materials Chemistry TU WienA-1060 ViennaAustria Department of Materials Science and Engineering University of CaliforniaBerkeley210 Hearst Memorial Mining BuildingBerkeleyUSA SpaceX 1 Rocket RoadHawthorneCA 90250USA Department of Physics and Science of Advanced Materials Program Central Michigan UniversityMount PleasantMI 48859USA Department of Physics and Department of Chemistry University of North TexasDentonTXUSA Materials Science Electrical EngineeringPhysics and ChemistryDuke UniversityDurhamNC 27708USA

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 *** 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 ***,the Automatic Anharmonic Phonon Library(AAPL)is *** 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 ***“experiment ***”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 *** AAPL with the pseudo-hybrid functional ACBN0 is possible to improve accuracy without increasing computational requirements.

关键词： properties. harmonic calculating

Discovery of high-entropy ceramics via machine learning

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npj Computational Materials 2020年第1期6卷 1323-1331页

作者： Kevin Kaufmann Daniel Maryanovsky William M.Mellor Chaoyi Zhu Alexander S.Rosengarten Tyler J.Harrington Corey Oses Cormac Toher stefano curtarolo Kenneth S.Vecchio Department of NanoEngineering UC San DiegoLa JollaCA 92093USA Department of Cognitive Science UC San DiegoLa JollaCA 92093USA Materials Science and Engineering Program UC San DiegoLa JollaCA 92093USA Department of Mechanical Engineering and Materials Science Duke UniversityDurhamNC 27708USA Materials Science Electrical EngineeringPhysics and ChemistryDuke UniversityDurhamNC 27708USA

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 *** approaches are based on physical intuition and/or expensive trial and error *** computational methods rely on the availability of sufficient experimental data and computational *** learning(ML)applied to materials science can accelerate development and reduce *** 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.

关键词： ceramics entropy attracting