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| 1 | Machine learning modeling of superconducting critical temperature显示文摘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. | Valentin Stanev Corey Oses A.Gilad Kusne Efrain Rodriguez Johnpierre Paglione Stefano Curtarolo Ichiro Takeuchi | 2018 | npj Computational Materials2018，，1： | 21 |
| 2 | Predicting superhard materials via a machine learning informed evolutionary structure search显示文摘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. | Patrick Avery Xiaoyu Wang Corey Oses Eric Gossett Davide M.Proserpio Cormac Toher Stefano Curtarolo Eva Zurek | 2019 | npj Computational Materials2019，，1： | 7 |
| 3 | Discovery of high-entropy ceramics via machine learning显示文摘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. | Kevin Kaufmann Daniel Maryanovsky William M.Mellor Chaoyi Zhu Alexander S.Rosengarten Tyler J.Harrington Corey Oses Cormac Toher Stefano Curtarolo Kenneth S.Vecchio | 2020 | npj Computational Materials2020，，1： | 5 |
| 4 | Coordination corrected ab initio formation enthalpies显示文摘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. | Rico Friedrich Demet Usanmaz Corey Oses Andrew Supka Marco Fornari Marco Buongiorno Nardelli Cormac Toher Stefano Curtarolo | 2019 | npj Computational Materials2019，，1： | 3 |
| 5 | Unavoidable disorder and entropy in multi-component systems显示文摘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. | Cormac Toher Corey Oses David Hicks Stefano Curtarolo | 2019 | npj Computational Materials2019，，1： | 1 |
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