FeSe is one of the most enigmatic superconductors.Among the family of iron-based compounds,it has the simplest chemical makeup and structure,and yet it displays superconducting transition temperature(T_(c))spanning 0 ...FeSe is one of the most enigmatic superconductors.Among the family of iron-based compounds,it has the simplest chemical makeup and structure,and yet it displays superconducting transition temperature(T_(c))spanning 0 to 15 K for thin films,while it is typically 8 K for single crystals.This large variation of T_(c)within one family underscores a key challenge associated with understanding superconductivity in iron chalcogenides.Here,using a dual-beam pulsed laser deposition(PLD)approach,we have fabricated a unique lattice-constant gradient thin film of FeSe which has revealed a clear relationship between the atomic structure and the superconducting transition temperature for the first time.The dual-beam PLD that generates laser fluence gradient inside the plasma plume has resulted in a continuous variation in distribution of edge dislocations within a single film,and a precise correlation between the lattice constant and T_(c)has been observed here,namely,T_(c)∝√c-c_(0),where c is the c-axis lattice constant(and c_(0)is a constant).This explicit relation in conjunction with a theoretical investigation indicates that it is the shifting of the dxy orbital of Fe which plays a governing role in the interplay between nematicity and superconductivity in FeSe.展开更多
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 bet...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.展开更多
Machine learning is becoming a valuable tool for scientific discovery.Particularly attractive is the application of machine learning methods to the field of materials development,which enables innovations by discoveri...Machine learning is becoming a valuable tool for scientific discovery.Particularly attractive is the application of machine learning methods to the field of materials development,which enables innovations by discovering new and better functional materials.To apply machine learning to actual materials development,close collaboration between scientists and machine learning tools is necessary.However,such collaboration has been so far impeded by the black box nature of many machine learning algorithms.It is often difficult for scientists to interpret the data-driven models from the viewpoint of material science and physics.Here,we demonstrate the development of spin-driven thermoelectric materials with anomalous Nernst effect by using an interpretable machine learning method called factorized asymptotic Bayesian inference hierarchical mixture of experts(FAB/HMEs).Based on prior knowledge of material science and physics,we were able to extract from the interpretable machine learning some surprising correlations and new knowledge about spin-driven thermoelectric materials.Guided by this,we carried out an actual material synthesis that led to the identification of a novel spin-driven thermoelectric material.This material shows the largest thermopower to date.展开更多
Analyzing large X-ray diffraction(XRD)datasets is a key step in high-throughput mapping of the compositional phase diagrams of combinatorial materials libraries.Optimizing and automating this task can help accelerate ...Analyzing large X-ray diffraction(XRD)datasets is a key step in high-throughput mapping of the compositional phase diagrams of combinatorial materials libraries.Optimizing and automating this task can help accelerate the process of discovery of materials with novel and desirable properties.Here,we report a new method for pattern analysis and phase extraction of XRD datasets.The method expands the Nonnegative Matrix Factorization method,which has been used previously to analyze such datasets,by combining it with custom clustering and cross-correlation algorithms.This new method is capable of robust determination of the number of basis patterns present in the data which,in turn,enables straightforward identification of any possible peak-shifted patterns.Peak-shifting arises due to continuous change in the lattice constants as a function of composition and is ubiquitous in XRD datasets from composition spread libraries.Successful identification of the peak-shifted patterns allows proper quantification and classification of the basis XRD patterns,which is necessary in order to decipher the contribution of each unique single-phase structure to the multi-phase regions.The process can be utilized to determine accurately the compositional phase diagram of a system under study.The presented method is applied to one synthetic and one experimental dataset and demonstrates robust accuracy and identification abilities.展开更多
基金supported by the National Natural Science Foundation of China(12225412,12204333,12374141,11834016,11927808)the National Key Basic Research Program of China(2021YFA0718700,2022YFA1403900,2022YFA1403000)+4 种基金the Strategic Priority Research Program(B)of Chinese Academy of Sciences(XDB25000000,XDB33000000)Beijing Natural Science Foundation(Z190008),the Beijing Nova Program of Science and Technology(20220484014)CAS Project for Young Scientists in Basic Research(2022YSBR-048)The Key-Area Research and Development Program of Guangdong Province(Grant No.2020B0101340002)The work at the University of Maryland was funded by AFOSR FA9550-14-10332 and NIST 60NANB19D027.
文摘FeSe is one of the most enigmatic superconductors.Among the family of iron-based compounds,it has the simplest chemical makeup and structure,and yet it displays superconducting transition temperature(T_(c))spanning 0 to 15 K for thin films,while it is typically 8 K for single crystals.This large variation of T_(c)within one family underscores a key challenge associated with understanding superconductivity in iron chalcogenides.Here,using a dual-beam pulsed laser deposition(PLD)approach,we have fabricated a unique lattice-constant gradient thin film of FeSe which has revealed a clear relationship between the atomic structure and the superconducting transition temperature for the first time.The dual-beam PLD that generates laser fluence gradient inside the plasma plume has resulted in a continuous variation in distribution of edge dislocations within a single film,and a precise correlation between the lattice constant and T_(c)has been observed here,namely,T_(c)∝√c-c_(0),where c is the c-axis lattice constant(and c_(0)is a constant).This explicit relation in conjunction with a theoretical investigation indicates that it is the shifting of the dxy orbital of Fe which plays a governing role in the interplay between nematicity and superconductivity in FeSe.
基金This research is supported by ONR N000141512222,ONR N00014-13-1-0635AFOSR No.FA 9550-14-10332.C.O.acknowledges support from the National Science Foundation Graduate Research Fellowship under grant No.DGF1106401+5 种基金J.P.acknowledges support from the Gordon and Betty Moore Foundation’s EPiQS Initiative through grant No.GBMF4419S.C.acknowledges support by the Alexander von Humboldt-FoundationThis research is supported by ONR N000141512222,ONR N00014-13-1-0635,and AFOSR no.FA 9550-14-10332C.O.acknowledges support from the National Science Foundation Graduate Research Fellowship under grant no.DGF1106401J.P.acknowledges support from the Gordon and Betty Moore Foundation’s EPiQS Initiative through grant no.GBMF4419S.C.acknowledges support by the Alexander von Humboldt-Foundation.
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.
基金This work was supported by JST-PRESTO“Advanced Materials Informatics through Comprehensive Integration among Theoretical,Experimental,Computational and Data-Centric Sciences”(Grant No.JPMJPR17N4)JST-ERATO“Spin Quantum Rectification Project”(Grant No.JPMJER1402)I.T.is supported in part by C-SPIN,one of six centers of STARnet,a Semiconductor Research Corporation program,sponsored by MARCO and DARPA.
文摘Machine learning is becoming a valuable tool for scientific discovery.Particularly attractive is the application of machine learning methods to the field of materials development,which enables innovations by discovering new and better functional materials.To apply machine learning to actual materials development,close collaboration between scientists and machine learning tools is necessary.However,such collaboration has been so far impeded by the black box nature of many machine learning algorithms.It is often difficult for scientists to interpret the data-driven models from the viewpoint of material science and physics.Here,we demonstrate the development of spin-driven thermoelectric materials with anomalous Nernst effect by using an interpretable machine learning method called factorized asymptotic Bayesian inference hierarchical mixture of experts(FAB/HMEs).Based on prior knowledge of material science and physics,we were able to extract from the interpretable machine learning some surprising correlations and new knowledge about spin-driven thermoelectric materials.Guided by this,we carried out an actual material synthesis that led to the identification of a novel spin-driven thermoelectric material.This material shows the largest thermopower to date.
基金Velimir V.Vesselinov and Boian S.Alexandrov were supported by LANL LDRD grant 20180060The work at UMD was funded by ONR N00014-13-1-0635,ONR 5289230 N000141512222the National Science Foundation,DMR-1505103.
文摘Analyzing large X-ray diffraction(XRD)datasets is a key step in high-throughput mapping of the compositional phase diagrams of combinatorial materials libraries.Optimizing and automating this task can help accelerate the process of discovery of materials with novel and desirable properties.Here,we report a new method for pattern analysis and phase extraction of XRD datasets.The method expands the Nonnegative Matrix Factorization method,which has been used previously to analyze such datasets,by combining it with custom clustering and cross-correlation algorithms.This new method is capable of robust determination of the number of basis patterns present in the data which,in turn,enables straightforward identification of any possible peak-shifted patterns.Peak-shifting arises due to continuous change in the lattice constants as a function of composition and is ubiquitous in XRD datasets from composition spread libraries.Successful identification of the peak-shifted patterns allows proper quantification and classification of the basis XRD patterns,which is necessary in order to decipher the contribution of each unique single-phase structure to the multi-phase regions.The process can be utilized to determine accurately the compositional phase diagram of a system under study.The presented method is applied to one synthetic and one experimental dataset and demonstrates robust accuracy and identification abilities.
