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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

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

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

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