To share and utilize data effectively for collaborative work,a common understanding of the knowledge behind the data,including its context and meaning,is a fundamental requirement.This paper focuses on the gaps that h...To share and utilize data effectively for collaborative work,a common understanding of the knowledge behind the data,including its context and meaning,is a fundamental requirement.This paper focuses on the gaps that hinder common understanding between the real world and the data space,acting as barriers between systems,organizations,data spaces,and disciplines.To understand the core reasons and devise strategies for bridging the gap,the author has endeavored to synthesize a case study of material data activities from two perspectives:diachronic and synchronic,which is framed into a two-step process,involving the establishment of intersubjectivity among experts and interobjectivity among materials/substances data.As a result,the author has formulated an action plan for the digitization of engineering materials,encompassing tacit knowledge,know-how,and intellectual property rights to establish a foundation for their use with traceability,interoperability,and reusability.In order to create a conceptual framework that enhances a productive ecosystem facilitated by networked materials and substance databases,this plan is conclusively based on two key steps:fostering interactions among experts rooted in substances and materials and standardizing digitized data related to substances/materials based on their geospatial structural information.展开更多
Following the Materials Genome Initiative project,materials research has embarked a new research paradigm centered around material repositories,significantly accelerating the discovery of novel materials,such as therm...Following the Materials Genome Initiative project,materials research has embarked a new research paradigm centered around material repositories,significantly accelerating the discovery of novel materials,such as thermoelectrics.Thermoelectric materials,capable of directly converting heat into electricity,are garnering increasing attention in applications like waste heat recovery and refrigeration.To facilitate research in this emerging paradigm,we have established the Materials Hub with Three-Dimensional Structures(MatHub-3d)repository,which serves as the foundation for high-throughput(HTP)calculations,property analysis,and the design of thermoelectric materials.In this review,we summarize recent advancements in thermoelectric materials powered by the MatHub-3d,specifically HTP calculations of transport properties and material design on key factors.For HTP calculations,we develop the electrical transport package for HTP purpose,and utilize it for materials screening.In some works,we investigate the relationship between transport properties and chemical bonds for particular types of thermoelectric compounds based on HTP results,enhancing the fundamental understanding about interested compounds.In our work associated with material design,we primarily utilize key factors beyond transport properties to further expedite materials screening and speedily identify specific materials for further theoretical/experimental analyses.Finally,we discuss the future developments of the MatHub-3d and the evolving directions of database-driven thermoelectric research.展开更多
Additive Manufacturing(AM)is revolutionizing aerospace,transportation,and biomedical sectors with its potential to create complex geometries.However,the metallic materials currently used in AM are not intended for hig...Additive Manufacturing(AM)is revolutionizing aerospace,transportation,and biomedical sectors with its potential to create complex geometries.However,the metallic materials currently used in AM are not intended for high-energy beam processes,suggesting performance improvement.The development of materials for AM still faces challenge because of the inefficient trial-and-error conventional methods.This review examines the challenges and current state of materials including aluminum alloys,titanium alloys,superalloys,and high-entropy alloys(HEA)in AM,and summarizes the high-throughput methods in alloy development for AM.In addition,the advantages of high-throughput preparation technology in improving the properties and optimizing the microstructure mechanism of major additive manufacturing alloys are described.This article concludes by emphasizing the importance of high-throughput techniques in pushing the boundaries of AM materials development,pointing toward a future of more effective and innovative material solutions.展开更多
Concentrated solid solution materials with huge compositional design space and normally unexpected property attract extensive interests of researchers.In these emerging materials,local composition fluctuation such as ...Concentrated solid solution materials with huge compositional design space and normally unexpected property attract extensive interests of researchers.In these emerging materials,local composition fluctuation such as short-range order(SRO),has been observed and found to have nontrivial effects on material properties,and thus can be utilized as an additional degree of freedom for material optimization.To exploit SRO,its interplay with factors beyond element-level property,including lattice symmetry and bonding environment,should be clarified.In this work by using layered transition-metal dichalcogenide Mo(X0.5X00.5)2(X/X0=O,S,Se,or Te)with mixed element in the non-metal sublattice as the platform,the ordering phenomena are systematically studied using multiscale simulations.As expected,electronegativity difference between X and X0 strongly regulates SRO.Additionally,SRO and long-range order(LRO)are observed in the 2H and T/T0 phase of MoXX0,respectively,indicating a strong influence of lattice symmetry on SRO.More importantly,as vdW interaction is introduced,the SRO structure in 2HMoXX0 bilayer can be re-configured,while the LRO in T/T0-MoXX0 remains unchanged.Electronic insights for SRO and the resultant property variation are obtained.This work presents a thorough understanding of SRO in bonding complex systems,benefiting the SRO-guided material designs.展开更多
Calcium carbonate(CaCO_(3))is a crucial mineral with great scientific relevance in biomineralization and geoscience.However,excessive precipitation of CaCO_(3)is posing a threat to industrial production and the aquati...Calcium carbonate(CaCO_(3))is a crucial mineral with great scientific relevance in biomineralization and geoscience.However,excessive precipitation of CaCO_(3)is posing a threat to industrial production and the aquatic environment.The utilization of chemical inhibitors is typically considered an economical and successful route for addressing the scaling issues,while the underlying mechanism is still debated and needs to be further investigated.In this context,a deep understanding of the crystallization process of CaCO_(3)and how the inhibitors interact with CaCO_(3)nuclei and crystals are of great significance in evaluating the performance of scale inhibitors.In recent years,with the rapid development of computing facilities,computer simulations have provided an atomic-level perspective on the kinetics and thermodynamics of possible association events in CaCO_(3)solutions as well as the predictions of nucleation pathway and growth mechanism of CaCO_(3)crystals as a complement to experiment.This review surveys several computational methods and their achievements in this field with a focus on analyzing the functional mechanisms of different types of inhibitors.A general discussion of the current challenges and future directions in applying atomistic simulations to the discovery,design,and development of more effective water-scale inhibitors is also discussed.展开更多
Mg alloy suffers from its poor corrosion resistance as a result of anodic dissolution of Mg and hydrogen evolution reaction(HER)in humid environments.In this study,the effects of alloying elements(Al,Zn,Y,Ce,and Mn)on...Mg alloy suffers from its poor corrosion resistance as a result of anodic dissolution of Mg and hydrogen evolution reaction(HER)in humid environments.In this study,the effects of alloying elements(Al,Zn,Y,Ce,and Mn)on both processes in Mg alloys have been quantitatively predicted.Using first-principle calculations,we first obtained the substitution energies of alloying elements to compare their segregation preference,and then analyzed the influence of solutes at different layers on the stability and hydrogen adsorption properties of Mg(0001)surface by calculating the formation enthalpy,surface energy,vacancy formation energy,work function,Bader charge,deformation charge density,and adsorption free energy of H atom.It has been found that,on the one hand,the interior Mn solute atoms reduce the dissolution of Mg atoms and the transfer of electrons,consequently slowing down the anodic dissolution process.On another hand,the Mn,Y,and Ce elements on the surface inhibit the cathodic HER process by elevating the absolute value of hydrogen adsorption free energy,as a result of those solutes effectively controlling H adsorption behavior on Mg(0001)surface.In contrast,all five elements dissolved inside the Mg grain do not show significant effects on the H adsorption behavior.展开更多
The emerging photovoltaic(PV)technologies,such as organic and perovskite PVs,have the characteristics of complex compositions and processing,resulting in a large multidimensional parameter space for the development an...The emerging photovoltaic(PV)technologies,such as organic and perovskite PVs,have the characteristics of complex compositions and processing,resulting in a large multidimensional parameter space for the development and optimization of the technologies.Traditional manual methods are time-consuming and laborintensive in screening and optimizing material properties.Materials genome engineering(MGE)advances an innovative approach that combines efficient experimentation,big database and artificial intelligence(AI)algorithms to accelerate materials research and development.High-throughput(HT)research platforms perform multidimensional experimental tasks rapidly,providing a large amount of reliable and consistent data for the creation of materials databases.Therefore,the development of novel experimental methods combining HT and AI can accelerate materials design and application,which is beneficial for establishing material-processing-property relationships and overcoming bottlenecks in the development of emerging PV technologies.This review introduces the key technologies involved in MGE and overviews the accelerating role of MGE in the field of organic and perovskite PVs.展开更多
Hierarchical clustering algorithm has been applied to identify the X-ray diffraction(XRD)patterns from a high-throughput characterization of the combinatorial materials chips.As data quality is usually correlated with...Hierarchical clustering algorithm has been applied to identify the X-ray diffraction(XRD)patterns from a high-throughput characterization of the combinatorial materials chips.As data quality is usually correlated with acquisition time,it is important to study the hierarchical clustering performance as a function of data quality in order to optimize the efficiency of high-throughput experiments.This work investigated the effects of signal-to-noise ratio on the performance of hier-archical clustering using 29 distance metrics for the XRD patterns from Fe−Co−Ni ternary combinatorial materials chip.It is found that the clustering accuracies evaluated by the F1 score only fluctuate slightly with signal-to-noise ratio varying from 15.5 to 22.3(dB)under the experimental condition.This suggests that although it may take 40-50 s to collect a visually high-quality diffraction pattern,the measurement time could be significantly reduced to as low as 4 s without substantial loss in phase identification accuracy by hierarchical clustering.Among the 29 distance metrics,Pearsonχ^(2)shows the highest mean F1 score of 0.77 and lowest standard deviation of 0.008.It shows that the distance matrixes calculated by Pearsonχ^(2)are mainly controlled by the XRD peak shifting characteristics and visualized by the metric multidimensional data scaling.展开更多
Materials genome engineering(MGE)has been successfully applied in various fields,resulting in a series of novel materials with excellent performance.Significant progress has been made in high-throughput simulation,exp...Materials genome engineering(MGE)has been successfully applied in various fields,resulting in a series of novel materials with excellent performance.Significant progress has been made in high-throughput simulation,experimentation,and data-driven techniques,enabling the effective prediction,rapid synthesis,and characterization of many classes of materials.In this brief review,we introduce the achievements made in the field of metallic glasses(MGs)using MGE,in particular high-throughput experimentation and data-driven approaches.High-throughput experiments help to efficiently synthesize and characterize many materials in a short period of time,enabling the construction of high-quality material databases for data-driven methods.Paired with machine learning,potential alloys of desired properties may be revealed and predicted.Along with the progress in computational power and algorithms of machine learning,the complex composition-structure-properties relationship is hopefully established,which in turn help efficient and precise prediction of new MGs.展开更多
Al-Si alloys are widely used in automotive casting components while microporosity has always been a detrimental defect that leads to property degradation.In this study,a coupled three-dimensional cellular automata(CA)...Al-Si alloys are widely used in automotive casting components while microporosity has always been a detrimental defect that leads to property degradation.In this study,a coupled three-dimensional cellular automata(CA)model has been used to predict the hydrogen porosity as functions of cooling rate and initial hydrogen concentration.By quantifying the pore characteristics,it has been found that the average equivalent pore diameter decreases from 40.43 to 23.98μm and the pore number density increases from 10.3 to 26.6 mm^(−3)as the cooling rate changes from 2.6 to 19.4℃/s at the initial hydrogen concentration of 0.25 mL/100 g.It is also notable that the pore size increases as the initial hydrogen concentration changes from 0.15 to 0.25 mL/100 g while the pore number remains stable.In addition,the linear regression between secondary dendrite arm spacing and the equivalent pore diameter has been studied for the first time,matching well with experiments.This work exhibits the application of CA model in future process optimization and robust condition design for advanced automotive parts made of Al-Si alloys.展开更多
For a long time,the phase-field method has been considered a mesoscale phenomenological method that lacks physical accuracy and is unable to be closely linked to the mechanical or functional properties of materials.So...For a long time,the phase-field method has been considered a mesoscale phenomenological method that lacks physical accuracy and is unable to be closely linked to the mechanical or functional properties of materials.Some misunderstandings existing in these viewpoints need to be clarified.Therefore,it is necessary to propose or adopt the perspective of“unified phase-field modeling(UPFM)”to address these issues,which means that phase-field modeling has multiple unified characteristics.Specifically,the phase-field method is the perfect unity of thermodynamics and kinetics,the unity of multi-scale models from microto meso and then to macro,the unity of internal or/and external driving energy with order parameters as field variables,the unity of multiple physical fields,and thus the unity of material composition design,process optimization,microstructure control,and performance prediction.It is precisely because the phase-field approach has these unified characteristics that,after more than 40 years of development,it has been increasingly widely applied in materials science and engineering.展开更多
Individual phases are commonly considered as the building blocks of materials.However,the accurate theoretical prediction of properties of individual phases remains elusive.The top-down approach by decoding genomic bu...Individual phases are commonly considered as the building blocks of materials.However,the accurate theoretical prediction of properties of individual phases remains elusive.The top-down approach by decoding genomic building blocks of individual phases from experimental observations is nonunique.The density functional theory(DFT),as a state-of-the-art solution of quantum mechanics,prescribes the existence of a ground-state configuration at 0 K for a given system.It is self-evident that the ground-state configuration alone is insufficient to describe a phase at finite temperatures as symmetry-breaking non-ground-state configurations are excited statistically at temperatures above 0 K.Our multiscale entropy approach(recently terms as Zentropy theory)postulates that the entropy of a phase is composed of the sum of the entropy of each configuration weighted by its probability plus the configurational entropy among all configurations.Consequently,the partition function of each configuration in statistical mechanics needs to be evaluated by its free energy rather than total energy.The combination of the ground-state and symmetry-breaking non-ground-state configurations represents the building blocks of materials and can be used to quantitatively predict free energy of individual phases with the free energy of each configuration predicted from DFT as well as all properties derived from free energy of individual phases。展开更多
Cu_(x)O with flower-like hierarchical structures has attracted significant research interest due to its intriguing morphologies and unique properties.The conventional methods for synthesizing such complex structures a...Cu_(x)O with flower-like hierarchical structures has attracted significant research interest due to its intriguing morphologies and unique properties.The conventional methods for synthesizing such complex structures are costly and require rigorous experimental conditions.Recently,the X-ray irradiation has emerged as a promising method for the rapid fabrication of precisely controlled Cu_(x)O shapes in large areas under environmentally friendly conditions.Nevertheless,the morphological regulation of the X-ray-induced synthesis of the Cu_(x)O is a multi-parameter optimization task.Therefore,it is essential to quantitatively reveal the interplay between these parameters and the resulting morphology.In this work,we employed a high-throughput experimental data-driven approach to investigate the kinetics of X-ray-induced reactions and the impact of key factors,including sputtering power,film thickness,and annealing of precursor Cu thin films on the morphologies of Cu_(x)O.For the first time,the flower-like Cu_(x)O nanostructures were synthesized using X-ray radiation at ambient condition.This research proposes an eco-friendly and cost-effective strategy for producing Cu_(x)O with customizable morphologies.Furthermore,it enhances comprehension of the underlying mechanisms of X-rayinduced morphological modification,which is essential for optimizing the synthesis process and expanding the potential applications of flower-like structures.展开更多
Extensive trial and error in the variable space is the main cause of low efficiency and high cost in material development.The experimental tasks can be reduced significantly in the case that the variable space is narr...Extensive trial and error in the variable space is the main cause of low efficiency and high cost in material development.The experimental tasks can be reduced significantly in the case that the variable space is narrowed down by reliable computer simulations.Because of their numerous variables in material design,however,the variable space is still too large to be accessed thoroughly even with a computational approach.High-throughput computations(HTC)make it possible to complete a material screening in a large space by replacing the conventionally manual and sequential operations with automatic,robust,and concurrent streamlines.The efficiency of HTC,which is one of the pillars of materials genome engineering,has been verified in many studies,but its applications are still limited by demanding computational costs.Introduction of data mining and artificial intelligence into HTC has become an effective approach to solve the problem.In the past years,many studies have focused on the development and application of HTC and data combined approaches,which is considered as a new paradigm in computational materials science.This review focuses on the main advances in the field of data-assisted HTC for material research and development and provides our outlook on its future development.展开更多
The integrated computational materials engineering(ICME)has achieved great success in accelerating the rational design and deployment of new materials.It is a new route of designing new materials and processes and hig...The integrated computational materials engineering(ICME)has achieved great success in accelerating the rational design and deployment of new materials.It is a new route of designing new materials and processes and highlighted by Materials Genome Initiative/Engineering that stresses the high-throughput computation in addition to high-throughput experimentation and materials informatics.This article presents a brief review on the basic theories and multi-scale computational tools of ICME to design advanced steel grades,including the first-principles calculations,the CALPHAD method(i.e.,computational thermodynamics)fueled by dedicated databases,diffusion and phase-field simulations,as well as finite analysis methods and machine learning.In the ICME scheme to deal with steels,the CALPHAD method is considered as the core to readily consider multi-component systems and integrated to link the microscopic simulations(such as diffusion and phase field method to predict microstructure evolutions in response to external conditions)and macroscopic finite analysis method to deal with mechanical properties.Two applications are also presented to address the new routes to carry out materials design,especially for advanced steels.展开更多
Aluminum-lithium(Al-Li)alloy is one of the most promising lightweight structural materials in the aeronautic and aerospace industries.The key to achieving their excellent mechanical properties lies in tailoring T1 str...Aluminum-lithium(Al-Li)alloy is one of the most promising lightweight structural materials in the aeronautic and aerospace industries.The key to achieving their excellent mechanical properties lies in tailoring T1 strengthening precipitates;however,the nucleation of such nanoparticles remains unknown.Combining atomic resolution HAADF-STEM with first-principles calculations based on the density functional theory(DFT),here,we report a counterintuitive nucleation mechanism of the T1 that evolves from an Eshelby inclusion with unstable stacking faults.This precursor is accelerated by Ag-Mg clusters to reduce the barrier,forming the structural framework.In addition,these Ag-Mg clusters trap the free Cu and Li to prepare the chemical compositions for T1.Our findings provide a new perspective on the phase transformations of complex precipitates through solute clusters in terms of geometric structure and chemical bonding functions.展开更多
Machine-learning interatomic potentials have revolutionized materials modeling at the atomic scale.Thanks to these,it is now indeed possible to perform simulations of ab initio quality over very large time and length ...Machine-learning interatomic potentials have revolutionized materials modeling at the atomic scale.Thanks to these,it is now indeed possible to perform simulations of ab initio quality over very large time and length scales.More recently,various universal machine-learning models have been proposed as an out-of-box approach avoiding the need to train and validate specific potentials for each particular material of interest.In this paper,we review and evaluate four different universal machine-learning interatomic potentials(uMLIPs),all based on graph neural network architectures which have demonstrated transferability from one chemical system to another.The evaluation procedure relies on data both from a recent verification study of density-functional-theory implementations and from the Materials Project.Through this comprehensive evaluation,we aim to provide guidance to materials scientists in selecting suitable models for their specific research problems,offer recommendations for model selection and optimization,and stimulate discussion on potential areas for improvement in current machinelearning methodologies in materials science.展开更多
Metal-organic frameworks(MOFs),renowned for structural diversity and design flexibility,exhibit potential in catalysis.However,the pursuit of higher catalytic activity through defects often compromises stability,requi...Metal-organic frameworks(MOFs),renowned for structural diversity and design flexibility,exhibit potential in catalysis.However,the pursuit of higher catalytic activity through defects often compromises stability,requiring a delicate balance.Traditional trial-and-error method for optimizing synthesis parameters within the complex chemical space is inefficient.Herein,taking the typical MOF UiO-66(Ce)as an illustrative example,a closed loop workflow is built,which integrates ma-chine learning(ML)-assissted prediction,multi-objective optimization(MOO)and experimental preparation to synergistically optimize the defect content and thermal stability of UiO-66(Ce)for efficient hydrogenation of dicyclopentadiene(DCPD).An automatic data extraction program ensures data accuracy,establishing a high-quality database.ML is employed to explore the intricate synthesis-structure-property correlations,enabling precise delineation of pure-phase subspace and accurate predictions of properties.After two iterations,MOO model identifies optimal protocols for high defect content(>40%)and thermal stability(>300℃).The optimized UiO-66(Ce)exhibits superior catalytic performance in hydroge-nation of DCPD,validating the precision and reliability of our methodology.This ML-assisted approach offers a valuable paradigm for solving the trade-off riddle in materials field.展开更多
The corrosion susceptibility of magnesium(Mg)alloys presents a significant challenge for their broad application.Although there have been extensive experimental and theoretical investigations,the corrosion mechanisms ...The corrosion susceptibility of magnesium(Mg)alloys presents a significant challenge for their broad application.Although there have been extensive experimental and theoretical investigations,the corrosion mechanisms of Mg alloys are still unclear,especially the anodic dissolution process.Here,a thorough theoretical investigation based on ab initio molecular dynamics and metadynamics simulations has been conducted to clarify the underlying corrosion mechanism of Mg anode and propose effective strategies for enhancing corrosion resistance.Through comprehensive analyses of interfacial structures and equilibrium potentials for Mg(0001)/H_(2)O interface models with different water thicknesses,the Mg(0001)/72 H_(2)O model is identified to be reasonable with−2.17 V vs.standard hydrogen electrode equi-librium potential.In addition,utilizing metadynamics,the free energy barrier for Mg dissolution is calculated to be 0.835 eV,enabling the theoretical determination of anodic polarization curves for pure Mg that aligns well with experimental data.Based on the Mg(0001)/72 H_(2)O model,we further explore the effects of various alloying elements on anodic corrosion resistance,among which Al and Mn alloying elements are found to enhance corrosion resistance of Mg.This study provides valuable atomic-scale insights into the corrosion mechanism of magnesium alloys,offering theoretical guidance for developing novel corrosion-resistant Mg alloys.展开更多
In recent years,interdisciplinary research has become increasingly popular within the scientific community.The fields of materials science and chemistry have also gradually begun to apply the machine learning technolo...In recent years,interdisciplinary research has become increasingly popular within the scientific community.The fields of materials science and chemistry have also gradually begun to apply the machine learning technology developed by scientists from computer science.Graph neural networks(GNNs)are new machine learning models with powerful feature extraction,relationship inference,and compositional generalization capabilities.These advantages drive researchers to design computational models to accelerate material property prediction and new materials design,dramatically reducing the cost of traditional experimental methods.This review focuses on the principles and applications of the GNNs.The basic concepts and advantages of the GNNs are first introduced and compared to the traditional machine learning and neural networks.Then,the principles and highlights of seven classic GNN models,namely crystal graph convolutional neural networks,iCGCNN,Orbital Graph Convolutional Neural Network,MatErials Graph Network,Global Attention mechanism with Graph Neural Network,Atomistic Line Graph Neural Network,and BonDNet are discussed.Their connections and differences are also summarized.Finally,insights and prospects are provided for the rapid development of GNNs in materials science at the atomic scale.展开更多
文摘To share and utilize data effectively for collaborative work,a common understanding of the knowledge behind the data,including its context and meaning,is a fundamental requirement.This paper focuses on the gaps that hinder common understanding between the real world and the data space,acting as barriers between systems,organizations,data spaces,and disciplines.To understand the core reasons and devise strategies for bridging the gap,the author has endeavored to synthesize a case study of material data activities from two perspectives:diachronic and synchronic,which is framed into a two-step process,involving the establishment of intersubjectivity among experts and interobjectivity among materials/substances data.As a result,the author has formulated an action plan for the digitization of engineering materials,encompassing tacit knowledge,know-how,and intellectual property rights to establish a foundation for their use with traceability,interoperability,and reusability.In order to create a conceptual framework that enhances a productive ecosystem facilitated by networked materials and substance databases,this plan is conclusively based on two key steps:fostering interactions among experts rooted in substances and materials and standardizing digitized data related to substances/materials based on their geospatial structural information.
基金supported by the National Key Research and Development Program of China(2021YFB3502200,2018YFB0703600,and 2019YFA0704901)the National Natural Science Foundation of China(52172216,92163212,and 12174242)+3 种基金the Key Research Project of Zhejiang Laboratory(2021PE0AC02)Zhang W also acknowledges the support from Guangdong Innovation Research Team Project(2017ZT07C062)Guangdong Provincial Key-Lab program(2019B030301001)Shenzhen Municipal Key-Lab program(ZDSYS20190902092905285).
文摘Following the Materials Genome Initiative project,materials research has embarked a new research paradigm centered around material repositories,significantly accelerating the discovery of novel materials,such as thermoelectrics.Thermoelectric materials,capable of directly converting heat into electricity,are garnering increasing attention in applications like waste heat recovery and refrigeration.To facilitate research in this emerging paradigm,we have established the Materials Hub with Three-Dimensional Structures(MatHub-3d)repository,which serves as the foundation for high-throughput(HTP)calculations,property analysis,and the design of thermoelectric materials.In this review,we summarize recent advancements in thermoelectric materials powered by the MatHub-3d,specifically HTP calculations of transport properties and material design on key factors.For HTP calculations,we develop the electrical transport package for HTP purpose,and utilize it for materials screening.In some works,we investigate the relationship between transport properties and chemical bonds for particular types of thermoelectric compounds based on HTP results,enhancing the fundamental understanding about interested compounds.In our work associated with material design,we primarily utilize key factors beyond transport properties to further expedite materials screening and speedily identify specific materials for further theoretical/experimental analyses.Finally,we discuss the future developments of the MatHub-3d and the evolving directions of database-driven thermoelectric research.
基金the financial support of the National Natural Science Foundation of China(No.:52171026)Beijing Natural Science Foundation(No.:2242043).
文摘Additive Manufacturing(AM)is revolutionizing aerospace,transportation,and biomedical sectors with its potential to create complex geometries.However,the metallic materials currently used in AM are not intended for high-energy beam processes,suggesting performance improvement.The development of materials for AM still faces challenge because of the inefficient trial-and-error conventional methods.This review examines the challenges and current state of materials including aluminum alloys,titanium alloys,superalloys,and high-entropy alloys(HEA)in AM,and summarizes the high-throughput methods in alloy development for AM.In addition,the advantages of high-throughput preparation technology in improving the properties and optimizing the microstructure mechanism of major additive manufacturing alloys are described.This article concludes by emphasizing the importance of high-throughput techniques in pushing the boundaries of AM materials development,pointing toward a future of more effective and innovative material solutions.
基金supported by the National Key Research and Development Program of China(Grant No.2022YFB3807200)the National Natural Science Foundation of China(No.52173216)the Fundamental Research Funds for the Central Universities.
文摘Concentrated solid solution materials with huge compositional design space and normally unexpected property attract extensive interests of researchers.In these emerging materials,local composition fluctuation such as short-range order(SRO),has been observed and found to have nontrivial effects on material properties,and thus can be utilized as an additional degree of freedom for material optimization.To exploit SRO,its interplay with factors beyond element-level property,including lattice symmetry and bonding environment,should be clarified.In this work by using layered transition-metal dichalcogenide Mo(X0.5X00.5)2(X/X0=O,S,Se,or Te)with mixed element in the non-metal sublattice as the platform,the ordering phenomena are systematically studied using multiscale simulations.As expected,electronegativity difference between X and X0 strongly regulates SRO.Additionally,SRO and long-range order(LRO)are observed in the 2H and T/T0 phase of MoXX0,respectively,indicating a strong influence of lattice symmetry on SRO.More importantly,as vdW interaction is introduced,the SRO structure in 2HMoXX0 bilayer can be re-configured,while the LRO in T/T0-MoXX0 remains unchanged.Electronic insights for SRO and the resultant property variation are obtained.This work presents a thorough understanding of SRO in bonding complex systems,benefiting the SRO-guided material designs.
基金the financial support from the Natural Sciences and Engineering Research Council(NSERC)under Alliance Program(grant no.ALLRP 557113-20)the Canada Research Chairs Program。
文摘Calcium carbonate(CaCO_(3))is a crucial mineral with great scientific relevance in biomineralization and geoscience.However,excessive precipitation of CaCO_(3)is posing a threat to industrial production and the aquatic environment.The utilization of chemical inhibitors is typically considered an economical and successful route for addressing the scaling issues,while the underlying mechanism is still debated and needs to be further investigated.In this context,a deep understanding of the crystallization process of CaCO_(3)and how the inhibitors interact with CaCO_(3)nuclei and crystals are of great significance in evaluating the performance of scale inhibitors.In recent years,with the rapid development of computing facilities,computer simulations have provided an atomic-level perspective on the kinetics and thermodynamics of possible association events in CaCO_(3)solutions as well as the predictions of nucleation pathway and growth mechanism of CaCO_(3)crystals as a complement to experiment.This review surveys several computational methods and their achievements in this field with a focus on analyzing the functional mechanisms of different types of inhibitors.A general discussion of the current challenges and future directions in applying atomistic simulations to the discovery,design,and development of more effective water-scale inhibitors is also discussed.
基金supported by the National Natural Science Foundation of China(grant number 52073030)the National Natural Science Foundation of China-Guangxi Joint Fund(U20A20276).
文摘Mg alloy suffers from its poor corrosion resistance as a result of anodic dissolution of Mg and hydrogen evolution reaction(HER)in humid environments.In this study,the effects of alloying elements(Al,Zn,Y,Ce,and Mn)on both processes in Mg alloys have been quantitatively predicted.Using first-principle calculations,we first obtained the substitution energies of alloying elements to compare their segregation preference,and then analyzed the influence of solutes at different layers on the stability and hydrogen adsorption properties of Mg(0001)surface by calculating the formation enthalpy,surface energy,vacancy formation energy,work function,Bader charge,deformation charge density,and adsorption free energy of H atom.It has been found that,on the one hand,the interior Mn solute atoms reduce the dissolution of Mg atoms and the transfer of electrons,consequently slowing down the anodic dissolution process.On another hand,the Mn,Y,and Ce elements on the surface inhibit the cathodic HER process by elevating the absolute value of hydrogen adsorption free energy,as a result of those solutes effectively controlling H adsorption behavior on Mg(0001)surface.In contrast,all five elements dissolved inside the Mg grain do not show significant effects on the H adsorption behavior.
基金the financial support from the National Natural Science Foundation of China(52394273 and 52373179).
文摘The emerging photovoltaic(PV)technologies,such as organic and perovskite PVs,have the characteristics of complex compositions and processing,resulting in a large multidimensional parameter space for the development and optimization of the technologies.Traditional manual methods are time-consuming and laborintensive in screening and optimizing material properties.Materials genome engineering(MGE)advances an innovative approach that combines efficient experimentation,big database and artificial intelligence(AI)algorithms to accelerate materials research and development.High-throughput(HT)research platforms perform multidimensional experimental tasks rapidly,providing a large amount of reliable and consistent data for the creation of materials databases.Therefore,the development of novel experimental methods combining HT and AI can accelerate materials design and application,which is beneficial for establishing material-processing-property relationships and overcoming bottlenecks in the development of emerging PV technologies.This review introduces the key technologies involved in MGE and overviews the accelerating role of MGE in the field of organic and perovskite PVs.
基金funded by the National Key Research and Development Program of China(Grant Nos.2021YFB370-2102 and 2017YFB0701900).
文摘Hierarchical clustering algorithm has been applied to identify the X-ray diffraction(XRD)patterns from a high-throughput characterization of the combinatorial materials chips.As data quality is usually correlated with acquisition time,it is important to study the hierarchical clustering performance as a function of data quality in order to optimize the efficiency of high-throughput experiments.This work investigated the effects of signal-to-noise ratio on the performance of hier-archical clustering using 29 distance metrics for the XRD patterns from Fe−Co−Ni ternary combinatorial materials chip.It is found that the clustering accuracies evaluated by the F1 score only fluctuate slightly with signal-to-noise ratio varying from 15.5 to 22.3(dB)under the experimental condition.This suggests that although it may take 40-50 s to collect a visually high-quality diffraction pattern,the measurement time could be significantly reduced to as low as 4 s without substantial loss in phase identification accuracy by hierarchical clustering.Among the 29 distance metrics,Pearsonχ^(2)shows the highest mean F1 score of 0.77 and lowest standard deviation of 0.008.It shows that the distance matrixes calculated by Pearsonχ^(2)are mainly controlled by the XRD peak shifting characteristics and visualized by the metric multidimensional data scaling.
基金support by the National Key Research and Development Program of China(grant no.2018YFA0703600)the National Natural Science Foundation of China(grant no.51825104).
文摘Materials genome engineering(MGE)has been successfully applied in various fields,resulting in a series of novel materials with excellent performance.Significant progress has been made in high-throughput simulation,experimentation,and data-driven techniques,enabling the effective prediction,rapid synthesis,and characterization of many classes of materials.In this brief review,we introduce the achievements made in the field of metallic glasses(MGs)using MGE,in particular high-throughput experimentation and data-driven approaches.High-throughput experiments help to efficiently synthesize and characterize many materials in a short period of time,enabling the construction of high-quality material databases for data-driven methods.Paired with machine learning,potential alloys of desired properties may be revealed and predicted.Along with the progress in computational power and algorithms of machine learning,the complex composition-structure-properties relationship is hopefully established,which in turn help efficient and precise prediction of new MGs.
基金supported by the National Natural Science Foundation of China(grant number 52073030)the National Natural Science Foundation of China-Guangxi Joint Fund(U20A20276).
文摘Al-Si alloys are widely used in automotive casting components while microporosity has always been a detrimental defect that leads to property degradation.In this study,a coupled three-dimensional cellular automata(CA)model has been used to predict the hydrogen porosity as functions of cooling rate and initial hydrogen concentration.By quantifying the pore characteristics,it has been found that the average equivalent pore diameter decreases from 40.43 to 23.98μm and the pore number density increases from 10.3 to 26.6 mm^(−3)as the cooling rate changes from 2.6 to 19.4℃/s at the initial hydrogen concentration of 0.25 mL/100 g.It is also notable that the pore size increases as the initial hydrogen concentration changes from 0.15 to 0.25 mL/100 g while the pore number remains stable.In addition,the linear regression between secondary dendrite arm spacing and the equivalent pore diameter has been studied for the first time,matching well with experiments.This work exhibits the application of CA model in future process optimization and robust condition design for advanced automotive parts made of Al-Si alloys.
基金supported by the National Natural Science Foundation of China(grant number 52074246).
文摘For a long time,the phase-field method has been considered a mesoscale phenomenological method that lacks physical accuracy and is unable to be closely linked to the mechanical or functional properties of materials.Some misunderstandings existing in these viewpoints need to be clarified.Therefore,it is necessary to propose or adopt the perspective of“unified phase-field modeling(UPFM)”to address these issues,which means that phase-field modeling has multiple unified characteristics.Specifically,the phase-field method is the perfect unity of thermodynamics and kinetics,the unity of multi-scale models from microto meso and then to macro,the unity of internal or/and external driving energy with order parameters as field variables,the unity of multiple physical fields,and thus the unity of material composition design,process optimization,microstructure control,and performance prediction.It is precisely because the phase-field approach has these unified characteristics that,after more than 40 years of development,it has been increasingly widely applied in materials science and engineering.
基金the support from Dorothy Pate Enright Professorship at Penn State,National Science Foundation(FAIN-2229690,CMMI-2226976,and CMMI-2050069)Department of Energy(DE-SC0023185,DENE0009288,DE-AR0001435,and DE-NE0008945)Office of Naval Research(N00014-21-1-2608),Army Research Lab,Air Force Research Office,National Aeronautics and pace Administration,and many industrial companies with the current grants shown in parenthesis.
文摘Individual phases are commonly considered as the building blocks of materials.However,the accurate theoretical prediction of properties of individual phases remains elusive.The top-down approach by decoding genomic building blocks of individual phases from experimental observations is nonunique.The density functional theory(DFT),as a state-of-the-art solution of quantum mechanics,prescribes the existence of a ground-state configuration at 0 K for a given system.It is self-evident that the ground-state configuration alone is insufficient to describe a phase at finite temperatures as symmetry-breaking non-ground-state configurations are excited statistically at temperatures above 0 K.Our multiscale entropy approach(recently terms as Zentropy theory)postulates that the entropy of a phase is composed of the sum of the entropy of each configuration weighted by its probability plus the configurational entropy among all configurations.Consequently,the partition function of each configuration in statistical mechanics needs to be evaluated by its free energy rather than total energy.The combination of the ground-state and symmetry-breaking non-ground-state configurations represents the building blocks of materials and can be used to quantitatively predict free energy of individual phases with the free energy of each configuration predicted from DFT as well as all properties derived from free energy of individual phases。
基金supported by the National Key Research and Development Program of China(Grant No.2021YFB37-02102)the“Shanghai Jiao Tong University Initiation Program for New Teachers”(No.AF0500207).
文摘Cu_(x)O with flower-like hierarchical structures has attracted significant research interest due to its intriguing morphologies and unique properties.The conventional methods for synthesizing such complex structures are costly and require rigorous experimental conditions.Recently,the X-ray irradiation has emerged as a promising method for the rapid fabrication of precisely controlled Cu_(x)O shapes in large areas under environmentally friendly conditions.Nevertheless,the morphological regulation of the X-ray-induced synthesis of the Cu_(x)O is a multi-parameter optimization task.Therefore,it is essential to quantitatively reveal the interplay between these parameters and the resulting morphology.In this work,we employed a high-throughput experimental data-driven approach to investigate the kinetics of X-ray-induced reactions and the impact of key factors,including sputtering power,film thickness,and annealing of precursor Cu thin films on the morphologies of Cu_(x)O.For the first time,the flower-like Cu_(x)O nanostructures were synthesized using X-ray radiation at ambient condition.This research proposes an eco-friendly and cost-effective strategy for producing Cu_(x)O with customizable morphologies.Furthermore,it enhances comprehension of the underlying mechanisms of X-rayinduced morphological modification,which is essential for optimizing the synthesis process and expanding the potential applications of flower-like structures.
基金financial support from the Natural Science Foundation of China(No.21973064 to DX and No.22173064 to MY).
文摘Extensive trial and error in the variable space is the main cause of low efficiency and high cost in material development.The experimental tasks can be reduced significantly in the case that the variable space is narrowed down by reliable computer simulations.Because of their numerous variables in material design,however,the variable space is still too large to be accessed thoroughly even with a computational approach.High-throughput computations(HTC)make it possible to complete a material screening in a large space by replacing the conventionally manual and sequential operations with automatic,robust,and concurrent streamlines.The efficiency of HTC,which is one of the pillars of materials genome engineering,has been verified in many studies,but its applications are still limited by demanding computational costs.Introduction of data mining and artificial intelligence into HTC has become an effective approach to solve the problem.In the past years,many studies have focused on the development and application of HTC and data combined approaches,which is considered as a new paradigm in computational materials science.This review focuses on the main advances in the field of data-assisted HTC for material research and development and provides our outlook on its future development.
文摘The integrated computational materials engineering(ICME)has achieved great success in accelerating the rational design and deployment of new materials.It is a new route of designing new materials and processes and highlighted by Materials Genome Initiative/Engineering that stresses the high-throughput computation in addition to high-throughput experimentation and materials informatics.This article presents a brief review on the basic theories and multi-scale computational tools of ICME to design advanced steel grades,including the first-principles calculations,the CALPHAD method(i.e.,computational thermodynamics)fueled by dedicated databases,diffusion and phase-field simulations,as well as finite analysis methods and machine learning.In the ICME scheme to deal with steels,the CALPHAD method is considered as the core to readily consider multi-component systems and integrated to link the microscopic simulations(such as diffusion and phase field method to predict microstructure evolutions in response to external conditions)and macroscopic finite analysis method to deal with mechanical properties.Two applications are also presented to address the new routes to carry out materials design,especially for advanced steels.
基金supported by the National Natural Science Foundation of China(grant number 52073030)National Natural Science Foundation of China-Guangxi Joint Fund(U20A20276).
文摘Aluminum-lithium(Al-Li)alloy is one of the most promising lightweight structural materials in the aeronautic and aerospace industries.The key to achieving their excellent mechanical properties lies in tailoring T1 strengthening precipitates;however,the nucleation of such nanoparticles remains unknown.Combining atomic resolution HAADF-STEM with first-principles calculations based on the density functional theory(DFT),here,we report a counterintuitive nucleation mechanism of the T1 that evolves from an Eshelby inclusion with unstable stacking faults.This precursor is accelerated by Ag-Mg clusters to reduce the barrier,forming the structural framework.In addition,these Ag-Mg clusters trap the free Cu and Li to prepare the chemical compositions for T1.Our findings provide a new perspective on the phase transformations of complex precipitates through solute clusters in terms of geometric structure and chemical bonding functions.
基金supported by the National Key Research and Development Program of China(2022YFE0141100 and 2023YFB3003005).
文摘Machine-learning interatomic potentials have revolutionized materials modeling at the atomic scale.Thanks to these,it is now indeed possible to perform simulations of ab initio quality over very large time and length scales.More recently,various universal machine-learning models have been proposed as an out-of-box approach avoiding the need to train and validate specific potentials for each particular material of interest.In this paper,we review and evaluate four different universal machine-learning interatomic potentials(uMLIPs),all based on graph neural network architectures which have demonstrated transferability from one chemical system to another.The evaluation procedure relies on data both from a recent verification study of density-functional-theory implementations and from the Materials Project.Through this comprehensive evaluation,we aim to provide guidance to materials scientists in selecting suitable models for their specific research problems,offer recommendations for model selection and optimization,and stimulate discussion on potential areas for improvement in current machinelearning methodologies in materials science.
基金supported by the National Key R&D Program of China(Grant No.2021YFB3500700)Beijing Natural Science Foundation(Grant No.L233011)Guangdong Basic and Applied Basic Research Foundation(Grant No.2022A1515010185).
文摘Metal-organic frameworks(MOFs),renowned for structural diversity and design flexibility,exhibit potential in catalysis.However,the pursuit of higher catalytic activity through defects often compromises stability,requiring a delicate balance.Traditional trial-and-error method for optimizing synthesis parameters within the complex chemical space is inefficient.Herein,taking the typical MOF UiO-66(Ce)as an illustrative example,a closed loop workflow is built,which integrates ma-chine learning(ML)-assissted prediction,multi-objective optimization(MOO)and experimental preparation to synergistically optimize the defect content and thermal stability of UiO-66(Ce)for efficient hydrogenation of dicyclopentadiene(DCPD).An automatic data extraction program ensures data accuracy,establishing a high-quality database.ML is employed to explore the intricate synthesis-structure-property correlations,enabling precise delineation of pure-phase subspace and accurate predictions of properties.After two iterations,MOO model identifies optimal protocols for high defect content(>40%)and thermal stability(>300℃).The optimized UiO-66(Ce)exhibits superior catalytic performance in hydroge-nation of DCPD,validating the precision and reliability of our methodology.This ML-assisted approach offers a valuable paradigm for solving the trade-off riddle in materials field.
基金supported by the National Key Research and Development Program of China(Nos.2020YFB1505901,2021YFB3501002)the National Natural Science Foundation of China(Grant No.22106103,General Program No.52072240)+1 种基金the Shanghai Science and Technology Committee(No.18511109300)the Science and Technology Commission of the CMC(2019JCJQZD27300).
文摘The corrosion susceptibility of magnesium(Mg)alloys presents a significant challenge for their broad application.Although there have been extensive experimental and theoretical investigations,the corrosion mechanisms of Mg alloys are still unclear,especially the anodic dissolution process.Here,a thorough theoretical investigation based on ab initio molecular dynamics and metadynamics simulations has been conducted to clarify the underlying corrosion mechanism of Mg anode and propose effective strategies for enhancing corrosion resistance.Through comprehensive analyses of interfacial structures and equilibrium potentials for Mg(0001)/H_(2)O interface models with different water thicknesses,the Mg(0001)/72 H_(2)O model is identified to be reasonable with−2.17 V vs.standard hydrogen electrode equi-librium potential.In addition,utilizing metadynamics,the free energy barrier for Mg dissolution is calculated to be 0.835 eV,enabling the theoretical determination of anodic polarization curves for pure Mg that aligns well with experimental data.Based on the Mg(0001)/72 H_(2)O model,we further explore the effects of various alloying elements on anodic corrosion resistance,among which Al and Mn alloying elements are found to enhance corrosion resistance of Mg.This study provides valuable atomic-scale insights into the corrosion mechanism of magnesium alloys,offering theoretical guidance for developing novel corrosion-resistant Mg alloys.
文摘In recent years,interdisciplinary research has become increasingly popular within the scientific community.The fields of materials science and chemistry have also gradually begun to apply the machine learning technology developed by scientists from computer science.Graph neural networks(GNNs)are new machine learning models with powerful feature extraction,relationship inference,and compositional generalization capabilities.These advantages drive researchers to design computational models to accelerate material property prediction and new materials design,dramatically reducing the cost of traditional experimental methods.This review focuses on the principles and applications of the GNNs.The basic concepts and advantages of the GNNs are first introduced and compared to the traditional machine learning and neural networks.Then,the principles and highlights of seven classic GNN models,namely crystal graph convolutional neural networks,iCGCNN,Orbital Graph Convolutional Neural Network,MatErials Graph Network,Global Attention mechanism with Graph Neural Network,Atomistic Line Graph Neural Network,and BonDNet are discussed.Their connections and differences are also summarized.Finally,insights and prospects are provided for the rapid development of GNNs in materials science at the atomic scale.
