BACKGROUND Venlafaxine,a serotonin-norepinephrine reuptake inhibitor,is widely prescribed for the treatment of major depressive disorder.At therapeutic dose,it is generally safe,with a low incidence of adverse effects...BACKGROUND Venlafaxine,a serotonin-norepinephrine reuptake inhibitor,is widely prescribed for the treatment of major depressive disorder.At therapeutic dose,it is generally safe,with a low incidence of adverse effects.However,massive venlafaxine inge-stion can cause serious cardiotoxicity,leading to life-threatening arrhythmias.CASE SUMMARY A 31-year-old woman with a history of depression ingested 14.8 g of venlafaxine along with 6 mg of estazolam and 6 mg of flunitrazepam.On admission,2 hours post-ingestion,she presented only with mild QTc prolongation.At 4 hours post-ingestion,she developed a generalized tonic-clonic seizure.Following endo-tracheal intubation,intravenous midazolam infusion was initiated and 50 g of activated charcoal was administered via a nasogastric tube.At 15 hours post-ingestion,she developed ventricular tachycardia that rapidly progressed to refr-actory ventricular fibrillation,which was successfully treated with veno-arterial extracorporeal membrane oxygenation.Toxicological analysis revealed serum ve-nlafaxine and O-desmethylvenlafaxine concentrations 17µg/mL and 10µg/mL,respectively,at 15 hours post-ingestion.CONCLUSION In cases of massive venlafaxine ingestion,continuous intensive monitoring,particularly of QTc,is essential for at least 24 hours,even when initial clinical signs are mild.If refractory ventricular arrhythmias occur,prompt ini-tiation of veno-arterial extracorporeal membrane oxygenation should be considered.展开更多
Ni-Ti-based shape memory alloys(SMAs)have found widespread use in the last 70 years,but improving their functional stability remains a key quest for more robust and advanced applications.Named for their ability to ret...Ni-Ti-based shape memory alloys(SMAs)have found widespread use in the last 70 years,but improving their functional stability remains a key quest for more robust and advanced applications.Named for their ability to retain their processed shape as a result of a reversible martensitic transformation,SMAs are highly sensitive to compositional variations.Alloying with ternary and quaternary elements to finetune the lattice parameters and the thermal hysteresis of an SMA,therefore,becomes a challenge in materials exploration.Combinatorial materials science allows streamlining of the synthesis process and data management from multiple characterization techniques.In this study,a composition spread of Ni-Ti-Cu-V thin-film library was synthesized by magnetron co-sputtering on a thermally oxidized Si wafer.Composition-dependent phase transformation temperature and microstructure were investigated and determined using high-throughput wavelength dispersive spectroscopy,synchrotron X-ray diffraction,and temperature-dependent resistance measurements.Of the 177 compositions in the materials library,32 were observed to have shape memory effect,of which five had zero or near-zero thermal hysteresis.These compositions provide flexibility in the operating temperature regimes that they can be used in.A phase map for the quaternary system and correlations of functional properties are discussed w让h respect to the local microstructure and composition of the thin-film library.展开更多
<span style="line-height:1.5;font-family:Verdana;">This research aims to obtain useful information for development of medical devices such as wound dressing and tissue anti-adhesive product, using a sp...<span style="line-height:1.5;font-family:Verdana;">This research aims to obtain useful information for development of medical devices such as wound dressing and tissue anti-adhesive product, using a spongy sheet composed of hyaluronic acid (HA) and collagen (Col). The spongy sheets were manufactured by freeze vacuum drying of HA and Col aqueous solution, followed by UV irradiation to introduce intermolecular crosslinks between Col molecules. These spongy sheets are referred to as Sponge-A (ratio of HA/Col = 5/1) and Sponge-B (ratio of HA/Col = 5/5). Both surfaces of Sponge-A and Sponge-B treated with UV irradiation for 15 minutes are referred to as Sponge-A-15 and Sponge-B-15, respectively. The weight change of spongy sheet was determined by immersing a peace of spongy sheet in water at 37°</span><span style="line-height:1.5;font-family:Verdana;">C</span><span style="line-height:1.5;font-family:Verdana;">. The weight of sponge-A-15 collected 1/2, 1, 3, 7 days after immersion in water was 63.5%, 62.1%, 56.6%, 54.4% of the original weight, respectively. The weight of Sponge-B-15 was 78.3%, 76.7%, 79.1%, 71.9% of the original weight, respectively. The weight change of spongy sheet was determined by immersing a peace of spongy sheet in water containing collagenase at 37°</span><span style="line-height:1.5;font-family:Verdana;">C</span><span style="line-height:1.5;font-family:Verdana;">. The weight of Sponge-A-15 collected 6, 8, 10, 12 hours after immersion in water containing collagenase (0.0005</span><span "="" style="line-height:1.5;"> </span><span style="line-height:1.5;font-family:Verdana;">w/v%) was 65.7%, 59.8%, 57.9%, 55.2% of the original weight, respectively. The weight of Sponge-B-15 was 63.5%, 52.1%, 42.0%, 43.2% of the original weight, respectively. This spongy sheet is considered to have the unique structure, where HA molecules are entrapped in an intermolecular cross-linked network structure of Col molecules. When immersed in water containing collagenase, the weight loss of spongy sheet is accelerated by easy extraction of HA molecules from the enzymatic degraded Col network structure. The performance of wound dressing and tissue anti-adhesive product is considered to depend on appropriate ratio of HA and Col, and also on appropriate rate of intermolecular crosslinks between Col molecules. These findings obtained in this study provide useful information for product development such as wound dressing and tissue anti-adhesive product.展开更多
As an essential component of the Materials Genome Initiative aiming to shorten the period of materials research and development, combinatorial synthesis and rapid characterization technologies have been playing a more...As an essential component of the Materials Genome Initiative aiming to shorten the period of materials research and development, combinatorial synthesis and rapid characterization technologies have been playing a more and more important role in exploring new materials and comprehensively understanding materials properties. In this review, we discuss the advantages of high-throughput experimental techniques in researches on superconductors. The evolution of combinatorial thin-film technology and several high-speed screening devices are briefly introduced. We emphasize the necessity to develop new high-throughput research modes such as a combination of high-throughput techniques and conventional methods.展开更多
An inter-component epitaxial strain-induced PbTiOa metastable phase is observed in a PbTiO3-GoFe2O4 epitaxial composite film, corresponding to the dielectric anomaly reported previously. High-resolution synchrotron ra...An inter-component epitaxial strain-induced PbTiOa metastable phase is observed in a PbTiO3-GoFe2O4 epitaxial composite film, corresponding to the dielectric anomaly reported previously. High-resolution synchrotron radiation X-ray diffraction and first principles calculation demonstrate the coexistence of different PbTi03 phases, even a possible morphotropie phase boundary in the film, elucidating the underlying microscopic rneehanism of the formation of Pb TiO3 metastable phase. This sheds light on the design and manipulation of electromechanical properties of epitaxial films, through the strain engineering.展开更多
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
Lack of rigorous reproducibility and validation are significant hurdles for scientific development across many fields.Materials science,in particular,encompasses a variety of experimental and theoretical approaches th...Lack of rigorous reproducibility and validation are significant hurdles for scientific development across many fields.Materials science,in particular,encompasses a variety of experimental and theoretical approaches that require careful benchmarking.Leaderboard efforts have been developed previously to mitigate these issues.However,a comprehensive comparison and benchmarking on an integrated platform with multiple data modalities with perfect and defect materials data is still lacking.This work introduces JARVIS-Leaderboard,an open-source and community-driven platform that facilitates benchmarking and enhances reproducibility.The platform allows users to set up benchmarks with customtasks and enables contributions in the form of dataset,code,and meta-data submissions.We cover the following materials design categories:Artificial Intelligence(AI),Electronic Structure(ES).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
Machine learning techniques have proven invaluable to manage the ever growing volume of materials research data produced as developments continue in high-throughput materials simulation,fabrication,and characterizatio...Machine learning techniques have proven invaluable to manage the ever growing volume of materials research data produced as developments continue in high-throughput materials simulation,fabrication,and characterization.In particular,machine learning techniques have been demonstrated for their utility in rapidly and automatically identifying potential composition-phase maps from structural data characterization of composition spread libraries,enabling rapid materials fabrication-structure-property analysis and functional materials discovery.A key issue in development of an automated phase-diagram determination method is the choice of dissimilarity measure,or kernel function.The desired measure reduces the impact of confounding structural data issues on analysis performance.The issues include peak height changes and peak shifting due to lattice constant change as a function of composition.In this work,we investigate the choice of dissimilarity measure in X-ray diffraction-based structure analysis and the choice of measure’s performance impact on automatic composition-phase map determination.Nine dissimilarity measures are investigated for their impact in analyzing X-ray diffraction patterns for a Fe-Co-Ni ternary alloy composition spread.The cosine,Pearson correlation coefficient,and Jensen-Shannon divergence measures are shown to provide the best performance in the presence of peak height change and peak shifting(due to lattice constant change)when the magnitude of peak shifting is unknown.With prior knowledge of the maximum peak shifting,dynamic time warping in a normalized constrained mode provides the best performance.This work also serves to demonstrate a strategy for rapid analysis of a large number of X-ray diffraction patterns in general beyond data from combinatorial libraries.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
Due to the unavailability of any specific countermeasure,the constantly spreading C0VID-19 pandemic could only be partially and temporarily slowed down by implementing regional lockdowns that force people to stay at h...Due to the unavailability of any specific countermeasure,the constantly spreading C0VID-19 pandemic could only be partially and temporarily slowed down by implementing regional lockdowns that force people to stay at home and prevent their movement.With the progression of the pandemic,a considerable subset of the population would have acquired post-infection immunity and the tests that reveal the postinfection immune status of individuals are the need of the hour.展开更多
Machine learning has emerged as a powerful tool for the analysis of mesoscopic and atomically resolved images and spectroscopy in electron and scanning probe microscopy,with the applications ranging from feature extra...Machine learning has emerged as a powerful tool for the analysis of mesoscopic and atomically resolved images and spectroscopy in electron and scanning probe microscopy,with the applications ranging from feature extraction to information compression and elucidation of relevant order parameters to inversion of imaging data to reconstruct structural models.However,the fundamental limitation of machine learning methods is their correlative nature,leading to extreme susceptibility to confounding factors.Here,we implement the workflow for causal analysis of structural scanning transmission electron microscopy(STEM)data and explore the interplay between physical and chemical effects in a ferroelectric perovskite across the ferroelectric–antiferroelectric phase transitions.展开更多
Over the last decade,scanning transmission electron microscopy(STEM)has emerged as a powerful tool for probing atomic structures of complex materials with picometer precision,opening the pathway toward exploring ferro...Over the last decade,scanning transmission electron microscopy(STEM)has emerged as a powerful tool for probing atomic structures of complex materials with picometer precision,opening the pathway toward exploring ferroelectric,ferroelastic,and chemical phenomena on the atomic scale.Analyses to date extracting a polarization signal from lattice coupled distortions in STEM imaging rely on discovery of atomic positions from intensity maxima/minima and subsequent calculation of polarization and other order parameter fields from the atomic displacements.Here,we explore the feasibility of polarization mapping directly from the analysis of STEM images using deep convolutional neural networks(DCNNs).In this approach,the DCNN is trained on the labeled part of the image(i.e.,for human labelling),and the trained network is subsequently applied to other images.We explore the effects of the choice of the descriptors(centered on atomic columns and grid-based),the effects of observational bias,and whether the network trained on one composition can be applied to a different one.This analysis demonstrates the tremendous potential of the DCNN for the analysis of high-resolution STEM imaging and spectral data and highlights the associated limitations.展开更多
Diuron is one of the most frequently applied herbicides in sugarcane farming in southern Japan,and Australia.In addition,it is used as a booster substance in copper-based antifouling paints.Due to these various uses,D...Diuron is one of the most frequently applied herbicides in sugarcane farming in southern Japan,and Australia.In addition,it is used as a booster substance in copper-based antifouling paints.Due to these various uses,Diuron is released into the marine environment;however,little information is available on gene expression in corals and their symbiotic algae exposed to Diuron.We investigated the efects of Diuron on stress-responsive gene expression in the hermatypic coral Acropora tenuis and its symbiotic dinofagellates.After seven days of exposure to 1µg/L and 10µg/L Diuron,no signifcant changes in the body colour of corals were observed.However,quantitative reverse transcription-polymerase chain reaction analyses revealed that the expression levels of stress-responsive genes,such as heat shock protein 90(HSP90),HSP70,and calreticulin(CALR),were signifcantly downregulated in corals exposed to 10µg/L of Diuron for seven days.Moreover,aquaglyceroporin was signifcantly downregulated in corals exposed to environmentally relevant concentrations of 1µg/L Diuron.In contrast,no such efects were observed on the expression levels of other stress-responsive genes,such as oxidative stress-responsive proteins,methionine adenosyltransferase,and green/red fuorescent proteins.Diuron exposure had no signifcant efect on the expression levels of HSP90,HSP70,or HSP40 in the symbiotic dinofagellates.These results suggest that stress-responsive genes,such as HSPs,respond diferently to Diuron in corals and their symbiotic dinofagellates and that A.tenuis HSPs and CALRs may be useful molecular biomarkers for predicting stress responses induced by the herbicide Diuron.展开更多
文摘BACKGROUND Venlafaxine,a serotonin-norepinephrine reuptake inhibitor,is widely prescribed for the treatment of major depressive disorder.At therapeutic dose,it is generally safe,with a low incidence of adverse effects.However,massive venlafaxine inge-stion can cause serious cardiotoxicity,leading to life-threatening arrhythmias.CASE SUMMARY A 31-year-old woman with a history of depression ingested 14.8 g of venlafaxine along with 6 mg of estazolam and 6 mg of flunitrazepam.On admission,2 hours post-ingestion,she presented only with mild QTc prolongation.At 4 hours post-ingestion,she developed a generalized tonic-clonic seizure.Following endo-tracheal intubation,intravenous midazolam infusion was initiated and 50 g of activated charcoal was administered via a nasogastric tube.At 15 hours post-ingestion,she developed ventricular tachycardia that rapidly progressed to refr-actory ventricular fibrillation,which was successfully treated with veno-arterial extracorporeal membrane oxygenation.Toxicological analysis revealed serum ve-nlafaxine and O-desmethylvenlafaxine concentrations 17µg/mL and 10µg/mL,respectively,at 15 hours post-ingestion.CONCLUSION In cases of massive venlafaxine ingestion,continuous intensive monitoring,particularly of QTc,is essential for at least 24 hours,even when initial clinical signs are mild.If refractory ventricular arrhythmias occur,prompt ini-tiation of veno-arterial extracorporeal membrane oxygenation should be considered.
基金The author thanks Tieren Gao,Peer Decker,Alan Savan,and Manfred Wuttig for fruitful discussions.The authors gratefully acknowledge funding support by the National Science Foundation Graduate Research Fellowship Program(DGE 1322106).
文摘Ni-Ti-based shape memory alloys(SMAs)have found widespread use in the last 70 years,but improving their functional stability remains a key quest for more robust and advanced applications.Named for their ability to retain their processed shape as a result of a reversible martensitic transformation,SMAs are highly sensitive to compositional variations.Alloying with ternary and quaternary elements to finetune the lattice parameters and the thermal hysteresis of an SMA,therefore,becomes a challenge in materials exploration.Combinatorial materials science allows streamlining of the synthesis process and data management from multiple characterization techniques.In this study,a composition spread of Ni-Ti-Cu-V thin-film library was synthesized by magnetron co-sputtering on a thermally oxidized Si wafer.Composition-dependent phase transformation temperature and microstructure were investigated and determined using high-throughput wavelength dispersive spectroscopy,synchrotron X-ray diffraction,and temperature-dependent resistance measurements.Of the 177 compositions in the materials library,32 were observed to have shape memory effect,of which five had zero or near-zero thermal hysteresis.These compositions provide flexibility in the operating temperature regimes that they can be used in.A phase map for the quaternary system and correlations of functional properties are discussed w让h respect to the local microstructure and composition of the thin-film library.
文摘<span style="line-height:1.5;font-family:Verdana;">This research aims to obtain useful information for development of medical devices such as wound dressing and tissue anti-adhesive product, using a spongy sheet composed of hyaluronic acid (HA) and collagen (Col). The spongy sheets were manufactured by freeze vacuum drying of HA and Col aqueous solution, followed by UV irradiation to introduce intermolecular crosslinks between Col molecules. These spongy sheets are referred to as Sponge-A (ratio of HA/Col = 5/1) and Sponge-B (ratio of HA/Col = 5/5). Both surfaces of Sponge-A and Sponge-B treated with UV irradiation for 15 minutes are referred to as Sponge-A-15 and Sponge-B-15, respectively. The weight change of spongy sheet was determined by immersing a peace of spongy sheet in water at 37°</span><span style="line-height:1.5;font-family:Verdana;">C</span><span style="line-height:1.5;font-family:Verdana;">. The weight of sponge-A-15 collected 1/2, 1, 3, 7 days after immersion in water was 63.5%, 62.1%, 56.6%, 54.4% of the original weight, respectively. The weight of Sponge-B-15 was 78.3%, 76.7%, 79.1%, 71.9% of the original weight, respectively. The weight change of spongy sheet was determined by immersing a peace of spongy sheet in water containing collagenase at 37°</span><span style="line-height:1.5;font-family:Verdana;">C</span><span style="line-height:1.5;font-family:Verdana;">. The weight of Sponge-A-15 collected 6, 8, 10, 12 hours after immersion in water containing collagenase (0.0005</span><span "="" style="line-height:1.5;"> </span><span style="line-height:1.5;font-family:Verdana;">w/v%) was 65.7%, 59.8%, 57.9%, 55.2% of the original weight, respectively. The weight of Sponge-B-15 was 63.5%, 52.1%, 42.0%, 43.2% of the original weight, respectively. This spongy sheet is considered to have the unique structure, where HA molecules are entrapped in an intermolecular cross-linked network structure of Col molecules. When immersed in water containing collagenase, the weight loss of spongy sheet is accelerated by easy extraction of HA molecules from the enzymatic degraded Col network structure. The performance of wound dressing and tissue anti-adhesive product is considered to depend on appropriate ratio of HA and Col, and also on appropriate rate of intermolecular crosslinks between Col molecules. These findings obtained in this study provide useful information for product development such as wound dressing and tissue anti-adhesive product.
基金Project supported by the National Key Basic Research Program of China(Grant Nos.2015CB921000,2016YFA0300301,2017YFA0303003,and 2017YFA0302902)the National Natural Science Foundation of China(Grant Nos.11674374,11804378,and 11574372)+3 种基金the Beijing Municipal Science and Technology Project(Grant No.Z161100002116011)the Key Research Program of Frontier Sciences,Chinese Academy of Sciences(Grant Nos.QYZDB-SSW-SLH008 and QYZDY-SSW-SLH001)the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDB07020100)the Opening Project of Wuhan National High Magnetic Field Center(Grant No.PHMFF2015008)
文摘As an essential component of the Materials Genome Initiative aiming to shorten the period of materials research and development, combinatorial synthesis and rapid characterization technologies have been playing a more and more important role in exploring new materials and comprehensively understanding materials properties. In this review, we discuss the advantages of high-throughput experimental techniques in researches on superconductors. The evolution of combinatorial thin-film technology and several high-speed screening devices are briefly introduced. We emphasize the necessity to develop new high-throughput research modes such as a combination of high-throughput techniques and conventional methods.
基金Supported by the National Basic Research Program of China under Grant Nos 2012CB922004 and 2010CB934501, and the National Natural Science Foundation of China under Grant Nos 11179008, 51021091 and 50772106.
文摘An inter-component epitaxial strain-induced PbTiOa metastable phase is observed in a PbTiO3-GoFe2O4 epitaxial composite film, corresponding to the dielectric anomaly reported previously. High-resolution synchrotron radiation X-ray diffraction and first principles calculation demonstrate the coexistence of different PbTi03 phases, even a possible morphotropie phase boundary in the film, elucidating the underlying microscopic rneehanism of the formation of Pb TiO3 metastable phase. This sheds light on the design and manipulation of electromechanical properties of epitaxial films, through the strain engineering.
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supported by the financial assistance award 70NANB19H117 from the U.S.Department of Commerce,National Institute of Standards and Technologysupported by the U.S.Department of Energy,Office of Science,Basic Energy Sciences,Materials Sciences and Engineering Division,as part of the Computational Materials Sciences Program and Center for Predictive Simulation of Functional Materials+5 种基金supported by the Center for Nanophase Materials Sciences,which is a US Department of Energy,Office of Science User Facility at Oak Ridge National LaboratoryAHR thanks the Supercomputer Center and San Diego Supercomputer Center through allocation DMR140031 from the Advanced Cyberinfrastructure Coordination Ecosystem:Services&Support(ACCESS)program,which is supported by National Science Foundation grants#2138259,#2138286,#2138307,#2137603,and#2138296supported by NIST award 70NANB19H005 and NSF award CMMI-2053929S.H.W.especially thanks to the NSF Non-Academic Research Internships for Graduate Students(INTERN)program(CBET-1845531)for supporting part of the work in NIST under the guidance of K.CA.M.K.acknowledges support from the School of Materials Engineering at Purdue University under startup account F.10023800.05.002support by the Federal Ministry of Education and Research(BMBF)under Grant No.01DM21001B(German-Canadian Materials Acceleration Center).
文摘Lack of rigorous reproducibility and validation are significant hurdles for scientific development across many fields.Materials science,in particular,encompasses a variety of experimental and theoretical approaches that require careful benchmarking.Leaderboard efforts have been developed previously to mitigate these issues.However,a comprehensive comparison and benchmarking on an integrated platform with multiple data modalities with perfect and defect materials data is still lacking.This work introduces JARVIS-Leaderboard,an open-source and community-driven platform that facilitates benchmarking and enhances reproducibility.The platform allows users to set up benchmarks with customtasks and enables contributions in the form of dataset,code,and meta-data submissions.We cover the following materials design categories:Artificial Intelligence(AI),Electronic Structure(ES).
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supported by NIST and NEC and partially supported by ONR N000141512222.
文摘Machine learning techniques have proven invaluable to manage the ever growing volume of materials research data produced as developments continue in high-throughput materials simulation,fabrication,and characterization.In particular,machine learning techniques have been demonstrated for their utility in rapidly and automatically identifying potential composition-phase maps from structural data characterization of composition spread libraries,enabling rapid materials fabrication-structure-property analysis and functional materials discovery.A key issue in development of an automated phase-diagram determination method is the choice of dissimilarity measure,or kernel function.The desired measure reduces the impact of confounding structural data issues on analysis performance.The issues include peak height changes and peak shifting due to lattice constant change as a function of composition.In this work,we investigate the choice of dissimilarity measure in X-ray diffraction-based structure analysis and the choice of measure’s performance impact on automatic composition-phase map determination.Nine dissimilarity measures are investigated for their impact in analyzing X-ray diffraction patterns for a Fe-Co-Ni ternary alloy composition spread.The cosine,Pearson correlation coefficient,and Jensen-Shannon divergence measures are shown to provide the best performance in the presence of peak height change and peak shifting(due to lattice constant change)when the magnitude of peak shifting is unknown.With prior knowledge of the maximum peak shifting,dynamic time warping in a normalized constrained mode provides the best performance.This work also serves to demonstrate a strategy for rapid analysis of a large number of X-ray diffraction patterns in general beyond data from combinatorial libraries.
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.
文摘Due to the unavailability of any specific countermeasure,the constantly spreading C0VID-19 pandemic could only be partially and temporarily slowed down by implementing regional lockdowns that force people to stay at home and prevent their movement.With the progression of the pandemic,a considerable subset of the population would have acquired post-infection immunity and the tests that reveal the postinfection immune status of individuals are the need of the hour.
基金The work at the University of Maryland was supported in part by the National Institute of Standards and Technology Cooperative Agreement 70NANB17H301the Center for Spintronic Materials in Advanced infoRmation Technologies(SMART)one of centers in nCORE,a Semiconductor Research Corporation(SRC)program sponsored by NSF and NISTA.N.M.work was partially supported by the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie(grant agreement No 778070).
文摘Machine learning has emerged as a powerful tool for the analysis of mesoscopic and atomically resolved images and spectroscopy in electron and scanning probe microscopy,with the applications ranging from feature extraction to information compression and elucidation of relevant order parameters to inversion of imaging data to reconstruct structural models.However,the fundamental limitation of machine learning methods is their correlative nature,leading to extreme susceptibility to confounding factors.Here,we implement the workflow for causal analysis of structural scanning transmission electron microscopy(STEM)data and explore the interplay between physical and chemical effects in a ferroelectric perovskite across the ferroelectric–antiferroelectric phase transitions.
基金This STEM effort is based upon work supported by the U.S.Department of Energy(DOE),Office of Science,Basic Energy Sciences(BES),Materials Sciences and Engineering Division(S.V.K.,C.T.N.).This ML effort is based upon work supported by the U.S.DOE,Office of Science,Office of Basic Energy Sciences Data,Artificial Intelligence and Machine Learning at DOE Scientific User Facilities(A.G.).The work was performed and partially supported(M.Z.)at Oak Ridge National Laboratory’s Center for Nanophase Materials Sciences(CNMS),a U.S.DOE,Office of Science User Facility.The work at the University of Maryland was supported in part by the National Institute of Standards and Technology Cooperative Agreement 70NANB17H301 and the Center for Spintronic Materials in Advanced Information Technologies(SMART)one of the centers in nCORE,a Semiconductor Research Corporation(SRC)program sponsored by NSF and NIST.The authors gratefully acknowledge Dr.Karren More(CNMS)for careful reading and editing the manuscript.
文摘Over the last decade,scanning transmission electron microscopy(STEM)has emerged as a powerful tool for probing atomic structures of complex materials with picometer precision,opening the pathway toward exploring ferroelectric,ferroelastic,and chemical phenomena on the atomic scale.Analyses to date extracting a polarization signal from lattice coupled distortions in STEM imaging rely on discovery of atomic positions from intensity maxima/minima and subsequent calculation of polarization and other order parameter fields from the atomic displacements.Here,we explore the feasibility of polarization mapping directly from the analysis of STEM images using deep convolutional neural networks(DCNNs).In this approach,the DCNN is trained on the labeled part of the image(i.e.,for human labelling),and the trained network is subsequently applied to other images.We explore the effects of the choice of the descriptors(centered on atomic columns and grid-based),the effects of observational bias,and whether the network trained on one composition can be applied to a different one.This analysis demonstrates the tremendous potential of the DCNN for the analysis of high-resolution STEM imaging and spectral data and highlights the associated limitations.
基金This work was supported by the Japan Society for the Promotion of Science(JSPS)Grants-in-Aid for Scientifc Research(KAKENHI)(Grant Nos.26281048 and 21H03619).
文摘Diuron is one of the most frequently applied herbicides in sugarcane farming in southern Japan,and Australia.In addition,it is used as a booster substance in copper-based antifouling paints.Due to these various uses,Diuron is released into the marine environment;however,little information is available on gene expression in corals and their symbiotic algae exposed to Diuron.We investigated the efects of Diuron on stress-responsive gene expression in the hermatypic coral Acropora tenuis and its symbiotic dinofagellates.After seven days of exposure to 1µg/L and 10µg/L Diuron,no signifcant changes in the body colour of corals were observed.However,quantitative reverse transcription-polymerase chain reaction analyses revealed that the expression levels of stress-responsive genes,such as heat shock protein 90(HSP90),HSP70,and calreticulin(CALR),were signifcantly downregulated in corals exposed to 10µg/L of Diuron for seven days.Moreover,aquaglyceroporin was signifcantly downregulated in corals exposed to environmentally relevant concentrations of 1µg/L Diuron.In contrast,no such efects were observed on the expression levels of other stress-responsive genes,such as oxidative stress-responsive proteins,methionine adenosyltransferase,and green/red fuorescent proteins.Diuron exposure had no signifcant efect on the expression levels of HSP90,HSP70,or HSP40 in the symbiotic dinofagellates.These results suggest that stress-responsive genes,such as HSPs,respond diferently to Diuron in corals and their symbiotic dinofagellates and that A.tenuis HSPs and CALRs may be useful molecular biomarkers for predicting stress responses induced by the herbicide Diuron.
