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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 | An efficient and accurate framework for calculating lattice thermal conductivity of solids:AFLOW-AAPL Automatic Anharmonic Phonon Library显示文摘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. | Jose J.Plata Pinku Nath Demet Usanmaz Jesus Carrete Cormac Toher Maarten de Jong Mark Asta Marco Fornari Marco Buongiorno Nardelli Stefano Curtarolo | 2017 | npj Computational Materials2017，，1： | 5 |
| 5 | 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 |
| 6 | Parametrically constrained geometry relaxations for high-throughput materials science显示文摘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. | Maja-Olivia Lenz Thomas A.R.Purcell David Hicks Stefano Curtarolo Matthias Scheffler Christian Carbogno | 2019 | npj Computational Materials2019，，1： | 2 |
| 7 | AFLOW-XtalFinder:a reliable choice to identify crystalline prototypes显示文摘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. | David Hicks Cormac Toher Denise CFord Frisco Rose Carlo De Santo Ohad Levy Michael J.Mehl Stefano Curtarolo | 2021 | npj Computational Materials2021，，1： | 2 |
| 8 | Ab initio lattice stability in comparison with CALPHAD lattice stability显示文摘 | Wang Y Curtarolo S Jiang C | 2004 | CALPHAD2004，28，： | 1 |
| 9 | Ab initio lattice stability in comparison with CALPHAD lattice stability显示文摘 | WANG Y CURTAROLO S JIANG C ARROYAVE R WANG T CEDER G CHEN L Q LIU Z K | 2004 | CALPHAD2004，28，1： | 1 |
| 10 | Low Thermal Conductivity and Triaxial Phononic Anisotropy of Sn Se显示文摘 | Carrete J Mingo N Curtarolo S | 2014 | Applied Physics Letters2014，105，10： | 1 |
| 11 | Ab initio lattice stability in comparison with CALPHAD lattice stability 显示文摘 | WANG Y CURTAROLO S JIANG C | 2004 | CALPHAD2004，28，1： | 1 |
| 12 | Theoretical study of metal borides stability 显示文摘 | Kolmogorov A N Curtarolo S | 2006 | Phys Rev B2006，74，22： | 1 |
| 13 | Abinitio latticestability incomparisonwithCALPHADlattice stability 显示文摘 | Wang Y Curtarolo S Jiang C | 2004 | CALPHAD2004，28，： | 1 |
| 14 | Ab initio lattice stability in comparison with CALPHAD lattice stability显示文摘 | Wang Y Curtarolo S Jiang C | 2004 | CALPHAD2004，28，1： | 1 |
| 15 | Ab initio lattice stability in comparison with CALPHAD lattice stability 显示文摘 | Wang Y Curtarolo S Jiang C | 2004 | CALPHAD2004，28，1： | 1 |
| 16 | 查看详情显示文摘 | Wang Y Curtarolo S Jiang C Arroyave R Wang T Ceder G Chert L Q Liu Z K | | 0，，： | 1 |
| 17 | Theoretical study of metal borides stability 显示文摘 | Kolmogorov A N Curtarolo S | 2006 | Physical Review B2006，74，22： | 1 |
| 18 | Ab initio lattice stability in comparison with CALPHAD lattice stability显示文摘 | WANG Yi CURTAROLO S JIANG Chao | 2004 | CALPHAD2004，28，1： | 1 |
| 19 | Ab initio lattice stability in comparison with CALPHAD lattice stability显示文摘 | Wang Y Curtarolo S Jiang C | 2004 | CALPHAD2004，28，1： | 1 |
| 20 | Accuracy of ab initio methods in predicting the crystal structures of metals: A review of 80 binary alloys显示文摘 | Curtarolo S Morgan D Ceder G | 2005 | Calphad2005，29，3： | 1 |
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