Integrated computational materials engineering(ICME)is to integrate multi-scale computational simulations and key experimental methods such as macroscopic,mesoscopic,and microscopic into the whole process of Al alloys...Integrated computational materials engineering(ICME)is to integrate multi-scale computational simulations and key experimental methods such as macroscopic,mesoscopic,and microscopic into the whole process of Al alloys design and development,which enables the design and development of Al alloys to upgrade from traditional empirical to the integration of compositionprocess-structure-mechanical property,thus greatly accelerating its development speed and reducing its development cost.This study combines calculation of phase diagram(CALPHAD),Finite element calculations,first principle calculations,and microstructure characterization methods to predict and regulate the formation and structure of composite precipitates from the design of highmodulus Al alloy compositions and optimize the casting process parameters to inhibit the formation of micropore defects in the casting process,and the final tensile strength of Al alloys reaches420 MPa and Young's modulus reaches more than 88 GPa,which achieves the design goal of the high strength and modulus Al alloys,and establishes a new mode of the design and development of the strength/modulus Al alloys.展开更多
Most of granular materials are highly heteroge- neous, composed of voids and particles with different sizes and shapes. Geological matter, soil and clay in nature, geo-structure, concrete, etc. are practical ex- ample...Most of granular materials are highly heteroge- neous, composed of voids and particles with different sizes and shapes. Geological matter, soil and clay in nature, geo-structure, concrete, etc. are practical ex- amples among them. From the microscopic view, a lo- cal region in the medium is occupied by particles with small but finite sizes and granular material is naturally modeled as an assembly of discrete particles in contacts On the other hand, the local region is identified with a material point in the overall structure and this discon- tinuous medium can then be represented by an effective continuum on the macroscopic level展开更多
A data identifier(DID)is an essential tag or label in all kinds of databases—particularly those related to integrated computational materials engineering(ICME),inheritable integrated intelligent manufacturing(I3M),an...A data identifier(DID)is an essential tag or label in all kinds of databases—particularly those related to integrated computational materials engineering(ICME),inheritable integrated intelligent manufacturing(I3M),and the Industrial Internet ofThings.With the guidance and quick acceleration of the developme nt of advanced materials,as envisioned by official documents worldwide,more investigations are required to construct relative numerical standards for material informatics.This work proposes a universal DID format consisting of a set of build chains,which aligns with the classical form of identifier in both international and national standards,such as ISO/IEC 29168-1:2000,GB/T 27766-2011,GA/T 543.2-2011,GM/T 0006-2012,GJB 7365-2011,SL 325-2014,SL 607-201&WS 363.2-2011,and QX/T 39-2005.Each build chain is made up of capital letters and numbers,with no symbols.Moreover,the total length of each build chain is not restricted,which follows the formation of the Universal Coded Character Set in the international standard of ISO/IEC 10646.Based on these rules,the proposed DID is flexible and convenient for extendi ng and sharing in and between various cloud-based platforms.Accordingly,classical two-dimensional(2D)codes,including the Hanxin Code,Lots Perception Matrix(LP)Code,Quick Response(Q.R)code,Grid Matrix(GM)code,and Data Matrix(DM)Code,can be constructed and precisely recognized and/or decoded by either smart phones or specific machines.By utilizing these 2D codes as the fingerprints of a set of data linked with cloud-based platforms,progress and updates in the composition-processing-structure-property-performance workflow process can be tracked spontaneously,paving a path to accelerate the discovery and manufacture of advanced materials and enhance research productivity,performance,and collaboration.展开更多
Stable isotopes have been routinely used in chemical sciences,medical treatment and agricultural research.Conventional technologies to produce high-purity isotopes entail lengthy separation processes that often suffer...Stable isotopes have been routinely used in chemical sciences,medical treatment and agricultural research.Conventional technologies to produce high-purity isotopes entail lengthy separation processes that often suffer from low selectivity and poor energy efficiency.Recent advances in nanoporous materials open up new opportunities for more efficient isotope enrichment and separation as the pore size and local chemical environment of such materials can be engineered with atomic precision.In this work,we demonstrate the unique capability of nanoporous membranes for the separation of stable carbon isotopes by computational screening a materials database consisting of 12,478 computation-ready,experimental metal-organic frameworks(MOFs).Nanoporous materials with the highest selectivity and membrane performance scores have been identified for separation of^(12)CH_4/^(13)CH_4 at the ambient condition(300 K).Analyzing the structural features and metal sites of the promising MOF candidates offers useful insights into membrane design to further improve the performance.An upper limit of the efficiency has been identified for the separation of^(12)CH_4/^(13)CH_4 with the existing MOFs and those variations by replacement of the metal sites.展开更多
In order to understand the influence of ordering behaviors on the thermodynamic and mechanical properties of multi-principal element alloys(MPEAs),the temperature-dependent thermodynamic properties and mechanical prop...In order to understand the influence of ordering behaviors on the thermodynamic and mechanical properties of multi-principal element alloys(MPEAs),the temperature-dependent thermodynamic properties and mechanical properties of FCC_CoNiV MPEAs were comparatively predicted,where the alloys were modeled as the ordered configurations based on our previously predicted site occupying fractions(SOFs),as well as disordered configuration based on traditional special quasi-random structure(SQS).The ordering behavior not only improves the thermodynamic stability of the structure,but also increases the elastic properties and Vickers hardness.For example,at 973 K,the predicted bulk modulus(B),shear modulus(G),Young’s modulus(E),and Vickers hardness(HV)of FCC_CoNiV MPEA based on SOFs configuration are 187.82,79.03,207.93,and 7.58 GPa,respectively,while the corresponded data are 172.58,57.45,155.14,and 4.64 GPa for the SQS configuration,respectively.The Vickers hardness predicted based on SOFs agrees considerably well with the available experimental data,while it is underestimated obviously based on SQS.展开更多
High entropy alloys(HEAs)have recently become a popular category of alloys,composed of five or more elements.These alloys are of particular interest in the field of materials due to their unique structure and excellen...High entropy alloys(HEAs)have recently become a popular category of alloys,composed of five or more elements.These alloys are of particular interest in the field of materials due to their unique structure and excellent properties.However,the multi-component nature of these alloys poses challenges to traditional calculation methods,necessitating the development of alternative approaches for their analysis.Machine learning,a branch of artificial intelligence,has emerged as a promising solution to address the complexity inherent in the composition and structure of HEAs.The present review focuses on the fundamental definition and process of machine learning and its application in the research field of HEAs.The primary focus of this research field is the prediction of phase structure,hardness,strength,thermodynamic properties,and catalytic properties.In addition,future perspectives on the challenges in this research area are also presented.展开更多
Correction to:npj Computational Materials https://doi.org/10.1038/s41524-025-01606-5,published online 24 May 2025 The wrong Supplementary file was originally published with this article;it has now been replaced with t...Correction to:npj Computational Materials https://doi.org/10.1038/s41524-025-01606-5,published online 24 May 2025 The wrong Supplementary file was originally published with this article;it has now been replaced with the correct.Also,there was a typo in the title and has been corrected.The original article has been corrected.Open Access This article is licensed under a Creative Commons Attribution 4.0 International License,which permits use,sharing,adaptation,distribution and reproduction in any medium or format,as long as you give appropriate credit to the original author(s)and the source,provide a link to the Creative Commons licence,and indicate if changes were made.The images or other third party material in this article are included in the article’s Creative Commons licence,unless indicated otherwise in a credit line to the material.If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use,you will need to obtain permission directly from the copyright holder.To view a copy of this licence,visit http://creativecommons.org/licenses/by/4.0/.展开更多
Constructing large-scale,high-quality computational materials databases is pivotal for advancing material simulation and design.However,two essential challenges are yet to be fully resolved in this field:acquiring com...Constructing large-scale,high-quality computational materials databases is pivotal for advancing material simulation and design.However,two essential challenges are yet to be fully resolved in this field:acquiring comprehensive atomic-level structural information and effectively executing large-scale computational tasks for these material structures on supercomputers.In this study,we present a methodology that adeptly couples Artificial Intelligence(AI)and High-Performance Computing(HPC)to establish a comprehensive computational database with diverse materials data.We propose an AI-driven pipeline with a periodic-E(3)-equivariant diffusion model for structure generation and a transformer-based property prediction model incorporating 3D geometric analysis for material structure evaluation,followed by calculations on selected structures using Density Functional Theory(DFT).Specifically,a high-throughput computing framework was developed for efficient execution of various CPU/GPU/IO-intensive tasks,capitalizing on the heterogeneous computing nodes and shared storage architecture of supercomputers.Based on our HPC-AI strategy,we generated approximately 10 million hypothetical crystal structures,constituting the most extensive crystal material database currently available.By leveraging 2,000 nodes of the Tianhe-2 supercomputer for high-throughput computations,we accomplished about 80,000 DFT calculation datasets within a span of three months.Our approach represents a data-driven paradigm for boosting the materials design practice.展开更多
Casting technology is a fundamental and irreplaceable method in advanced manufacturing.The design and optimization of casting processes are crucial for producing high-performance,complex metal components.Transitioning...Casting technology is a fundamental and irreplaceable method in advanced manufacturing.The design and optimization of casting processes are crucial for producing high-performance,complex metal components.Transitioning from traditional process design based on"experience+experiment"to an integrated,intelligent approach is essential for achieving precise control over microstructure and properties.This paper provides a comprehensive and systematic review of intelligent casting process design and optimization for the first time.First,it explores process design methods based on casting simulation and integrated computational materials engineering(ICME).It then examines the application of machine learning(ML)in process design,highlighting its efficiency and existing challenges,along with the development of integrated intelligent design platforms.Finally,future research directions are discussed to drive further advancements and sustainable development in intelligent casting design and optimization.展开更多
Computational materials science increasingly benefits from data management,automation,and algorithm-based decision-making for the simulation of material properties and behavior.Experimental materials science also chan...Computational materials science increasingly benefits from data management,automation,and algorithm-based decision-making for the simulation of material properties and behavior.Experimental materials science also changes rapidly by incorporation of‘machine learning’in materials discovery campaigns.The benefits including automation,reproducibility,data provenance,and reusability of managed data,however,are not widely available in the experimental domain.We present an implementation of an Active Learning loop with an interface to an experimental measurement device in pyiron as a demonstrator how to combine experimental and simulated data in one framework.Apart from the acceleration provided through active learning,additional acceleration of the experimental characterization is achieved by using prior knowledge from density functional theory simulations as well as predictions based on text mining using correlations in word embeddings.With data from all domains in the same framework,an untapped potential for the acceleration of materials characterization and materials discovery campaigns becomes available.展开更多
To address the challenge of limited experimental materials data,extensive physical property databases are being developed based on high-throughput computational experiments,such as molecular dynamics simulations.Previ...To address the challenge of limited experimental materials data,extensive physical property databases are being developed based on high-throughput computational experiments,such as molecular dynamics simulations.Previous studies have shown that fine-tuning a predictor pretrained on a computational database to a real system can result in models with outstanding generalization capabilities compared to learning from scratch.This study demonstrates the scaling law of simulationto-real(Sim2Real)transfer learning for several machine learning tasks in materials science.Case studies of three prediction tasks for polymers and inorganic materials reveal that the prediction error on real systems decreases according to a power-law as the size of the computational data increases.Observing the scaling behavior offers various insights for database development,such as determining the sample size necessary to achieve a desired performance,identifying equivalent sample sizes for physical and computational experiments,and guiding the design of data production protocols for downstream real-world tasks.展开更多
Materials informatics has emerged as a promisingly new paradigm for accelerating materials discovery and design.It exploits the intelligent power of machine learning methods in massive materials data from experiments ...Materials informatics has emerged as a promisingly new paradigm for accelerating materials discovery and design.It exploits the intelligent power of machine learning methods in massive materials data from experiments or simulations to seek new materials,functionality,and principles,etc.Developing specialized facilities to generate,collect,manage,learn,and mine large-scale materials data is crucial to materials informatics.We herein developed an artificial-intelligence-aided data-driven infrastructure named Jilin Artificial-intelligence aided Materials-design Integrated Package(JAMIP),which is an open-source Python framework to meet the research requirements of computational materials informatics.It is integrated by materials production factory,high-throughput first-principles calculations engine,automatic tasks submission and monitoring progress,data extraction,management and storage system,and artificial intelligence machine learning based data mining functions.We have integrated specific features such as an inorganic crystal structure prototype database to facilitate high-throughput calculations and essential modules associated with machine learning studies of functional materials.We demonstrated how our developed code is useful in exploring materials informatics of optoelectronic semiconductors by taking halide perovskites as typical case.By obeying the principles of automation,extensibility,reliability,and intelligence,the JAMIP code is a promisingly powerful tool contributing to the fast-growing field of computational materials informatics.展开更多
Mg-Al alloys have excellent strength and ductility but relatively low thermal conductivity due to Al addition.The accurate prediction of thermal conductivity is a prerequisite for designing Mg-Al alloys with high ther...Mg-Al alloys have excellent strength and ductility but relatively low thermal conductivity due to Al addition.The accurate prediction of thermal conductivity is a prerequisite for designing Mg-Al alloys with high thermal conductivity.Thus,databases for predicting temperature-and composition-dependent thermal conductivities must be established.In this study,Mg-Al-La alloys with different contents of Al2La,Al3La,and Al11La3phases and solid solubility of Al in the α-Mg phase were designed.The influence of the second phase(s) and Al solid solubility on thermal conductivity was investigated.Experimental results revealed a second phase transformation from Al_(2)La to Al_(3)La and further to Al_(11)La_(3)with the increasing Al content at a constant La amount.The degree of the negative effect of the second phase(s) on thermal diffusivity followed the sequence of Al2La>Al3La>Al_(11)La_(3).Compared with the second phase,an increase in the solid solubility of Al in α-Mg remarkably reduced the thermal conductivity.On the basis of the experimental data,a database of the reciprocal thermal diffusivity of the Mg-Al-La system was established by calculation of the phase diagram (CALPHAD)method.With a standard error of±1.2 W/(m·K),the predicted results were in good agreement with the experimental data.The established database can be used to design Mg-Al alloys with high thermal conductivity and provide valuable guidance for expanding their application prospects.展开更多
The physics that associated with the performance of lithium secondary batteries(LSB)are reviewed.The key physical problems in LSB include the electronic conduction mechanism,kinetics and thermodynamics of lithium ion ...The physics that associated with the performance of lithium secondary batteries(LSB)are reviewed.The key physical problems in LSB include the electronic conduction mechanism,kinetics and thermodynamics of lithium ion migration,electrode/electrolyte surface/interface,structural(phase)and thermodynamics stability of the electrode materials,physics of intercalation and deintercalation.The relationship between the physical/chemical nature of the LSB materials and the batteries performance is summarized and discussed.A general thread of computational materials design for LSB materials is emphasized concerning all the discussed physics problems.In order to fasten the progress of the new materials discovery and design for the next generation LSB,the Materials Genome Initiative(MGI)for LSB materials is a promising strategy and the related requirements are highlighted.展开更多
Metal additive manufacturing(MAM)technology has experienced rapid development in recent years.As both equipment and materials progress towards increased maturity and commercialization,material metallurgy technology ba...Metal additive manufacturing(MAM)technology has experienced rapid development in recent years.As both equipment and materials progress towards increased maturity and commercialization,material metallurgy technology based on high energy sources has become a key factor influencing the future development of MAM.The calculation of phase diagrams(CALPHAD)is an essential method and tool for constructing multi-component phase diagrams by employing experimental phase diagrams and Gibbs free energy models of simple systems.By combining with the element mobility data and non-equilibrium phase transition model,it has been widely used in the analysis of traditional metal materials.The development of CALPHAD application technology for MAM is focused on the compositional design of printable materials,the reduction of metallurgical imperfections,and the control of microstructural attributes.This endeavor carries considerable theoretical and practical significance.This paper summarizes the important achievements of CALPHAD in additive manufacturing(AM)technology in recent years,including material design,process parameter optimization,microstructure evolution simulation,and properties prediction.Finally,the limitations of applying CALPHAD technology to MAM technology are discussed,along with prospective research directions.展开更多
Artificial intelligent aided design and manufacturing have been recognized as one kind of robust data-driven and data-intensive technologies in the integrated computational material engi-neering(ICME)era.Motivated by ...Artificial intelligent aided design and manufacturing have been recognized as one kind of robust data-driven and data-intensive technologies in the integrated computational material engi-neering(ICME)era.Motivated by the dramatical developments of the services of China Railway High-speed series for more than a decade,it is essential to reveal the foundations of lifecycle man-agement of those trains under environmental conditions.Here,the smart design and manufacturing of welded Q350 steel frames of CR200J series are introduced,presenting the capability and opportu-nity of ICME in weight reduction and lifecycle management at a cost-effective approach.In order to address the required fatigue life time enduring more than 9×10^(6)km,the response of optimized frames to the static and the dynamic loads are comprehensively investigated.It is highlighted that the maximum residual stress of the optimized welded frame is reduced to 69 MPa from 477 MPa of previous existing one.Based on the measured stress and acceleration from the railways,the fatigue life of modified frame under various loading modes could fulfil the requirements of the lifecycle man-agement.Moreover,our recent developed intelligent quality control strategy of welding process mediated by machine learning is also introduced,envisioning its application in the intelligent weld-ing.展开更多
Recent advances in machine learning have demonstrated an enormous utility of deep learning approaches,particularly Graph Neural Networks(GNNs)for materials science.These methods have emerged as powerful tools for high...Recent advances in machine learning have demonstrated an enormous utility of deep learning approaches,particularly Graph Neural Networks(GNNs)for materials science.These methods have emerged as powerful tools for high-throughput prediction of material properties,offering a compelling enhancement and alternative to traditional first-principles calculations.While the community has predominantly focused on developing increasingly complex and universal models to enhance predictive accuracy,such approaches often lack physical interpretability and insights into materials behavior.Here,we introduce a novel computational paradigm—Self-Adaptable Graph Attention Networks integrated with Symbolic Regression(SA-GAT-SR)—that synergistically combines the predictive capability of GNNs with the interpretative power of symbolic regression.Our framework employs a self-adaptable encoding algorithm that automatically identifies and adjust attention weights so as to screen critical features from an expansive 180-dimensional feature space while maintainingO(n)computational scaling.The integratedSRmodule subsequently distills these features into compact analytical expressions that explicitly reveal quantum-mechanically meaningful relationships,achieving 23×acceleration compared to conventional SR implementations that heavily rely on first-principle calculations-derived features as input.This work suggests a new framework in computational materials science,bridging the gap between predictive accuracy and physical interpretability,offering valuable physical insights into material behavior.展开更多
Correction to:npj Computational Materials https://doi.org/10.1038/s41524-025-01633-2,published online 08 May 2025 In this article the value “0.250” of Table 1 was incorrectly written as “250”. The original article...Correction to:npj Computational Materials https://doi.org/10.1038/s41524-025-01633-2,published online 08 May 2025 In this article the value “0.250” of Table 1 was incorrectly written as “250”. The original article has been corrected .展开更多
Nicolas Roisin,Guillaume Brunin,Gian-Marco Rignanese,Denis Flandre,Jean-Pierre Raskin&Samuel PoncéCorrection to: npj Computational Materials https://doi.org/10.1038/s41524-024-01425-0, published online 12 Oct...Nicolas Roisin,Guillaume Brunin,Gian-Marco Rignanese,Denis Flandre,Jean-Pierre Raskin&Samuel PoncéCorrection to: npj Computational Materials https://doi.org/10.1038/s41524-024-01425-0, published online 12 October 2024In this article, references 9, 11, 18-21, 34, 41, 44, 45, 47, 49, 56 and 62 were incomplete in the reference list. which have now beenupdated, and the original article has been corrected.展开更多
基金supported by the National Natural Science Foundation of China(No.52073030)。
文摘Integrated computational materials engineering(ICME)is to integrate multi-scale computational simulations and key experimental methods such as macroscopic,mesoscopic,and microscopic into the whole process of Al alloys design and development,which enables the design and development of Al alloys to upgrade from traditional empirical to the integration of compositionprocess-structure-mechanical property,thus greatly accelerating its development speed and reducing its development cost.This study combines calculation of phase diagram(CALPHAD),Finite element calculations,first principle calculations,and microstructure characterization methods to predict and regulate the formation and structure of composite precipitates from the design of highmodulus Al alloy compositions and optimize the casting process parameters to inhibit the formation of micropore defects in the casting process,and the final tensile strength of Al alloys reaches420 MPa and Young's modulus reaches more than 88 GPa,which achieves the design goal of the high strength and modulus Al alloys,and establishes a new mode of the design and development of the strength/modulus Al alloys.
文摘Most of granular materials are highly heteroge- neous, composed of voids and particles with different sizes and shapes. Geological matter, soil and clay in nature, geo-structure, concrete, etc. are practical ex- amples among them. From the microscopic view, a lo- cal region in the medium is occupied by particles with small but finite sizes and granular material is naturally modeled as an assembly of discrete particles in contacts On the other hand, the local region is identified with a material point in the overall structure and this discon- tinuous medium can then be represented by an effective continuum on the macroscopic level
基金This work was financially supported by the National Key Research and Development Program of China(2018YFB0703801,2018YFB0703802,2016YFB0701303,and 2016YFB0701304)CRRC Tangshan Co.,Ltd.(201750463031).Special thanks to Professor Hong Wang at Shanghai Jiao Tong University for the fruitful discussions and the constructive suggestions/comments.
文摘A data identifier(DID)is an essential tag or label in all kinds of databases—particularly those related to integrated computational materials engineering(ICME),inheritable integrated intelligent manufacturing(I3M),and the Industrial Internet ofThings.With the guidance and quick acceleration of the developme nt of advanced materials,as envisioned by official documents worldwide,more investigations are required to construct relative numerical standards for material informatics.This work proposes a universal DID format consisting of a set of build chains,which aligns with the classical form of identifier in both international and national standards,such as ISO/IEC 29168-1:2000,GB/T 27766-2011,GA/T 543.2-2011,GM/T 0006-2012,GJB 7365-2011,SL 325-2014,SL 607-201&WS 363.2-2011,and QX/T 39-2005.Each build chain is made up of capital letters and numbers,with no symbols.Moreover,the total length of each build chain is not restricted,which follows the formation of the Universal Coded Character Set in the international standard of ISO/IEC 10646.Based on these rules,the proposed DID is flexible and convenient for extendi ng and sharing in and between various cloud-based platforms.Accordingly,classical two-dimensional(2D)codes,including the Hanxin Code,Lots Perception Matrix(LP)Code,Quick Response(Q.R)code,Grid Matrix(GM)code,and Data Matrix(DM)Code,can be constructed and precisely recognized and/or decoded by either smart phones or specific machines.By utilizing these 2D codes as the fingerprints of a set of data linked with cloud-based platforms,progress and updates in the composition-processing-structure-property-performance workflow process can be tracked spontaneously,paving a path to accelerate the discovery and manufacture of advanced materials and enhance research productivity,performance,and collaboration.
基金financially supported by the National Science Foundation Harnessing the Data Revolution Big Idea under Grant No.NSF 1940118supported by the State Key Laboratory of Chemical Engineering(SKL-CHE20)。
文摘Stable isotopes have been routinely used in chemical sciences,medical treatment and agricultural research.Conventional technologies to produce high-purity isotopes entail lengthy separation processes that often suffer from low selectivity and poor energy efficiency.Recent advances in nanoporous materials open up new opportunities for more efficient isotope enrichment and separation as the pore size and local chemical environment of such materials can be engineered with atomic precision.In this work,we demonstrate the unique capability of nanoporous membranes for the separation of stable carbon isotopes by computational screening a materials database consisting of 12,478 computation-ready,experimental metal-organic frameworks(MOFs).Nanoporous materials with the highest selectivity and membrane performance scores have been identified for separation of^(12)CH_4/^(13)CH_4 at the ambient condition(300 K).Analyzing the structural features and metal sites of the promising MOF candidates offers useful insights into membrane design to further improve the performance.An upper limit of the efficiency has been identified for the separation of^(12)CH_4/^(13)CH_4 with the existing MOFs and those variations by replacement of the metal sites.
基金financially supported by the State Administration for Market Regulation,China(No.2021MK050)the National Natural Science Foundation of China(Nos.50971043,51171046,21973012)+3 种基金the Key Research and Development Program of China(Nos.2022YFB3807200,CISRI-21T62450ZD)the Natural Science Foundation of Fujian Province,China(Nos.2021J01590,2020J01351,2018J01754,2020J01474)the Student Research and Training Program(SRTP) of Fuzhou University,China(No.29320)Fujian Provincial Department of Science & Technology,China(No.2021H6011)。
文摘In order to understand the influence of ordering behaviors on the thermodynamic and mechanical properties of multi-principal element alloys(MPEAs),the temperature-dependent thermodynamic properties and mechanical properties of FCC_CoNiV MPEAs were comparatively predicted,where the alloys were modeled as the ordered configurations based on our previously predicted site occupying fractions(SOFs),as well as disordered configuration based on traditional special quasi-random structure(SQS).The ordering behavior not only improves the thermodynamic stability of the structure,but also increases the elastic properties and Vickers hardness.For example,at 973 K,the predicted bulk modulus(B),shear modulus(G),Young’s modulus(E),and Vickers hardness(HV)of FCC_CoNiV MPEA based on SOFs configuration are 187.82,79.03,207.93,and 7.58 GPa,respectively,while the corresponded data are 172.58,57.45,155.14,and 4.64 GPa for the SQS configuration,respectively.The Vickers hardness predicted based on SOFs agrees considerably well with the available experimental data,while it is underestimated obviously based on SQS.
基金financially supported by the National Natural Science Foundation of China(Project No.52473236,62304109)Natural Science Foundation of Xinjiang Uygur Autonomous Region of China(Project No.2024D01C62).
文摘High entropy alloys(HEAs)have recently become a popular category of alloys,composed of five or more elements.These alloys are of particular interest in the field of materials due to their unique structure and excellent properties.However,the multi-component nature of these alloys poses challenges to traditional calculation methods,necessitating the development of alternative approaches for their analysis.Machine learning,a branch of artificial intelligence,has emerged as a promising solution to address the complexity inherent in the composition and structure of HEAs.The present review focuses on the fundamental definition and process of machine learning and its application in the research field of HEAs.The primary focus of this research field is the prediction of phase structure,hardness,strength,thermodynamic properties,and catalytic properties.In addition,future perspectives on the challenges in this research area are also presented.
文摘Correction to:npj Computational Materials https://doi.org/10.1038/s41524-025-01606-5,published online 24 May 2025 The wrong Supplementary file was originally published with this article;it has now been replaced with the correct.Also,there was a typo in the title and has been corrected.The original article has been corrected.Open Access This article is licensed under a Creative Commons Attribution 4.0 International License,which permits use,sharing,adaptation,distribution and reproduction in any medium or format,as long as you give appropriate credit to the original author(s)and the source,provide a link to the Creative Commons licence,and indicate if changes were made.The images or other third party material in this article are included in the article’s Creative Commons licence,unless indicated otherwise in a credit line to the material.If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use,you will need to obtain permission directly from the copyright holder.To view a copy of this licence,visit http://creativecommons.org/licenses/by/4.0/.
基金supported by the National Key R&D Program of China(Grant No.2023YFB3002202)the Guangdong Provincial Key Area R&D Program of Guangdong Provincial(Grant No.2024B0101040005)the National Natural Science Foundation of China(Grant No.62461146204).
文摘Constructing large-scale,high-quality computational materials databases is pivotal for advancing material simulation and design.However,two essential challenges are yet to be fully resolved in this field:acquiring comprehensive atomic-level structural information and effectively executing large-scale computational tasks for these material structures on supercomputers.In this study,we present a methodology that adeptly couples Artificial Intelligence(AI)and High-Performance Computing(HPC)to establish a comprehensive computational database with diverse materials data.We propose an AI-driven pipeline with a periodic-E(3)-equivariant diffusion model for structure generation and a transformer-based property prediction model incorporating 3D geometric analysis for material structure evaluation,followed by calculations on selected structures using Density Functional Theory(DFT).Specifically,a high-throughput computing framework was developed for efficient execution of various CPU/GPU/IO-intensive tasks,capitalizing on the heterogeneous computing nodes and shared storage architecture of supercomputers.Based on our HPC-AI strategy,we generated approximately 10 million hypothetical crystal structures,constituting the most extensive crystal material database currently available.By leveraging 2,000 nodes of the Tianhe-2 supercomputer for high-throughput computations,we accomplished about 80,000 DFT calculation datasets within a span of three months.Our approach represents a data-driven paradigm for boosting the materials design practice.
基金supported by the National Natural Science Foundation of China(No.52074246)the National Defense Basic Scientific Research Program of China(No.JCKY2020408B002)+1 种基金the Key R&D Program of Shanxi Province(No.202102050201011)the Shanxi Province Graduate Innovation Project(No.2021Y591).
文摘Casting technology is a fundamental and irreplaceable method in advanced manufacturing.The design and optimization of casting processes are crucial for producing high-performance,complex metal components.Transitioning from traditional process design based on"experience+experiment"to an integrated,intelligent approach is essential for achieving precise control over microstructure and properties.This paper provides a comprehensive and systematic review of intelligent casting process design and optimization for the first time.First,it explores process design methods based on casting simulation and integrated computational materials engineering(ICME).It then examines the application of machine learning(ML)in process design,highlighting its efficiency and existing challenges,along with the development of integrated intelligent design platforms.Finally,future research directions are discussed to drive further advancements and sustainable development in intelligent casting design and optimization.
基金funding from Deutsche Forschungsgemeinschaft(DFG)through project LU1175/26-1LZ and MS gratefully acknowledge the financial support provided by the China Scholarship Council(CSC number:202208360048)MS,LB,JN,and AL gratefully acknowledge funding by Deutsche Forschungsgemeinschaft(DFG)for CRC1625,project number 506711657,subprojects A01,A04,A05,INF.
文摘Computational materials science increasingly benefits from data management,automation,and algorithm-based decision-making for the simulation of material properties and behavior.Experimental materials science also changes rapidly by incorporation of‘machine learning’in materials discovery campaigns.The benefits including automation,reproducibility,data provenance,and reusability of managed data,however,are not widely available in the experimental domain.We present an implementation of an Active Learning loop with an interface to an experimental measurement device in pyiron as a demonstrator how to combine experimental and simulated data in one framework.Apart from the acceleration provided through active learning,additional acceleration of the experimental characterization is achieved by using prior knowledge from density functional theory simulations as well as predictions based on text mining using correlations in word embeddings.With data from all domains in the same framework,an untapped potential for the acceleration of materials characterization and materials discovery campaigns becomes available.
基金support from MEXT as“Program for Promoting Researches on the Supercomputer Fugaku”(project ID:hp210264)JST CREST(Grant Numbers JPMJCR19I3,JPMJCR22O3,JPMJCR2332)+5 种基金MEXT/JSPS KAKENHI Grant-in-Aid for Scientific Research on Innovative Areas(19H05820)Grant-in-Aid for Scientific Research(A)(19H01132)Grant-in-Aid for Research Activity Start-up(23K19980)Grant-in-Aid for Scientific Research(C)(22K11949)Computational resources were provided by Fugaku at the RIKEN Center for Computational Science,Kobe,Japan(hp210264)the supercomputer at the Research Center for Computational Science,Okazaki,Japan(project:23-IMS-C113,24-IMS-C107).
文摘To address the challenge of limited experimental materials data,extensive physical property databases are being developed based on high-throughput computational experiments,such as molecular dynamics simulations.Previous studies have shown that fine-tuning a predictor pretrained on a computational database to a real system can result in models with outstanding generalization capabilities compared to learning from scratch.This study demonstrates the scaling law of simulationto-real(Sim2Real)transfer learning for several machine learning tasks in materials science.Case studies of three prediction tasks for polymers and inorganic materials reveal that the prediction error on real systems decreases according to a power-law as the size of the computational data increases.Observing the scaling behavior offers various insights for database development,such as determining the sample size necessary to achieve a desired performance,identifying equivalent sample sizes for physical and computational experiments,and guiding the design of data production protocols for downstream real-world tasks.
基金supported by the National Natural Science Foundation of China(61722403,92061113,and 12004131)the Interdisciplinary Research Grant for Ph Ds of Jilin University(101832020DJX043)。
文摘Materials informatics has emerged as a promisingly new paradigm for accelerating materials discovery and design.It exploits the intelligent power of machine learning methods in massive materials data from experiments or simulations to seek new materials,functionality,and principles,etc.Developing specialized facilities to generate,collect,manage,learn,and mine large-scale materials data is crucial to materials informatics.We herein developed an artificial-intelligence-aided data-driven infrastructure named Jilin Artificial-intelligence aided Materials-design Integrated Package(JAMIP),which is an open-source Python framework to meet the research requirements of computational materials informatics.It is integrated by materials production factory,high-throughput first-principles calculations engine,automatic tasks submission and monitoring progress,data extraction,management and storage system,and artificial intelligence machine learning based data mining functions.We have integrated specific features such as an inorganic crystal structure prototype database to facilitate high-throughput calculations and essential modules associated with machine learning studies of functional materials.We demonstrated how our developed code is useful in exploring materials informatics of optoelectronic semiconductors by taking halide perovskites as typical case.By obeying the principles of automation,extensibility,reliability,and intelligence,the JAMIP code is a promisingly powerful tool contributing to the fast-growing field of computational materials informatics.
基金financially supported by the National Key Research and Development Program of China (No.2021YFB3701001)the National Natural Science Foundation of China (No.U2102212)+1 种基金the Shanghai Rising-Star Program (No.21QA1403200)the Shanghai Engineering Research Center for Metal Parts Green Remanufacture (No.19DZ2252900) from Shanghai Engineering Research Center Construction Project。
文摘Mg-Al alloys have excellent strength and ductility but relatively low thermal conductivity due to Al addition.The accurate prediction of thermal conductivity is a prerequisite for designing Mg-Al alloys with high thermal conductivity.Thus,databases for predicting temperature-and composition-dependent thermal conductivities must be established.In this study,Mg-Al-La alloys with different contents of Al2La,Al3La,and Al11La3phases and solid solubility of Al in the α-Mg phase were designed.The influence of the second phase(s) and Al solid solubility on thermal conductivity was investigated.Experimental results revealed a second phase transformation from Al_(2)La to Al_(3)La and further to Al_(11)La_(3)with the increasing Al content at a constant La amount.The degree of the negative effect of the second phase(s) on thermal diffusivity followed the sequence of Al2La>Al3La>Al_(11)La_(3).Compared with the second phase,an increase in the solid solubility of Al in α-Mg remarkably reduced the thermal conductivity.On the basis of the experimental data,a database of the reciprocal thermal diffusivity of the Mg-Al-La system was established by calculation of the phase diagram (CALPHAD)method.With a standard error of±1.2 W/(m·K),the predicted results were in good agreement with the experimental data.The established database can be used to design Mg-Al alloys with high thermal conductivity and provide valuable guidance for expanding their application prospects.
基金supported by the National Natural Science Foundation of China(Grant Nos.11234013,11064004 and 11264014)supported by the"Gan-po talent 555"project of Jiangxi Province
文摘The physics that associated with the performance of lithium secondary batteries(LSB)are reviewed.The key physical problems in LSB include the electronic conduction mechanism,kinetics and thermodynamics of lithium ion migration,electrode/electrolyte surface/interface,structural(phase)and thermodynamics stability of the electrode materials,physics of intercalation and deintercalation.The relationship between the physical/chemical nature of the LSB materials and the batteries performance is summarized and discussed.A general thread of computational materials design for LSB materials is emphasized concerning all the discussed physics problems.In order to fasten the progress of the new materials discovery and design for the next generation LSB,the Materials Genome Initiative(MGI)for LSB materials is a promising strategy and the related requirements are highlighted.
基金supported by the National Key Research and Development Program of China(No.2021YFB3702500)。
文摘Metal additive manufacturing(MAM)technology has experienced rapid development in recent years.As both equipment and materials progress towards increased maturity and commercialization,material metallurgy technology based on high energy sources has become a key factor influencing the future development of MAM.The calculation of phase diagrams(CALPHAD)is an essential method and tool for constructing multi-component phase diagrams by employing experimental phase diagrams and Gibbs free energy models of simple systems.By combining with the element mobility data and non-equilibrium phase transition model,it has been widely used in the analysis of traditional metal materials.The development of CALPHAD application technology for MAM is focused on the compositional design of printable materials,the reduction of metallurgical imperfections,and the control of microstructural attributes.This endeavor carries considerable theoretical and practical significance.This paper summarizes the important achievements of CALPHAD in additive manufacturing(AM)technology in recent years,including material design,process parameter optimization,microstructure evolution simulation,and properties prediction.Finally,the limitations of applying CALPHAD technology to MAM technology are discussed,along with prospective research directions.
基金supported by the National Basic Scientific Research Project of China (No.JCKY2020607B003)CRRC (No.202CDA001)
文摘Artificial intelligent aided design and manufacturing have been recognized as one kind of robust data-driven and data-intensive technologies in the integrated computational material engi-neering(ICME)era.Motivated by the dramatical developments of the services of China Railway High-speed series for more than a decade,it is essential to reveal the foundations of lifecycle man-agement of those trains under environmental conditions.Here,the smart design and manufacturing of welded Q350 steel frames of CR200J series are introduced,presenting the capability and opportu-nity of ICME in weight reduction and lifecycle management at a cost-effective approach.In order to address the required fatigue life time enduring more than 9×10^(6)km,the response of optimized frames to the static and the dynamic loads are comprehensively investigated.It is highlighted that the maximum residual stress of the optimized welded frame is reduced to 69 MPa from 477 MPa of previous existing one.Based on the measured stress and acceleration from the railways,the fatigue life of modified frame under various loading modes could fulfil the requirements of the lifecycle man-agement.Moreover,our recent developed intelligent quality control strategy of welding process mediated by machine learning is also introduced,envisioning its application in the intelligent weld-ing.
基金supported by National Natural Science Foundation of China (No.12374057)Fundamental Research Funds for the Central Universities. The work (S.T.) at Los Alamos National Laboratory (LANL) was performed at the Center for Integrated Nanotechnologies (CINT), a U.S. Department of Energy, Office of Science user facility at LANL.
文摘Recent advances in machine learning have demonstrated an enormous utility of deep learning approaches,particularly Graph Neural Networks(GNNs)for materials science.These methods have emerged as powerful tools for high-throughput prediction of material properties,offering a compelling enhancement and alternative to traditional first-principles calculations.While the community has predominantly focused on developing increasingly complex and universal models to enhance predictive accuracy,such approaches often lack physical interpretability and insights into materials behavior.Here,we introduce a novel computational paradigm—Self-Adaptable Graph Attention Networks integrated with Symbolic Regression(SA-GAT-SR)—that synergistically combines the predictive capability of GNNs with the interpretative power of symbolic regression.Our framework employs a self-adaptable encoding algorithm that automatically identifies and adjust attention weights so as to screen critical features from an expansive 180-dimensional feature space while maintainingO(n)computational scaling.The integratedSRmodule subsequently distills these features into compact analytical expressions that explicitly reveal quantum-mechanically meaningful relationships,achieving 23×acceleration compared to conventional SR implementations that heavily rely on first-principle calculations-derived features as input.This work suggests a new framework in computational materials science,bridging the gap between predictive accuracy and physical interpretability,offering valuable physical insights into material behavior.
文摘Correction to:npj Computational Materials https://doi.org/10.1038/s41524-025-01633-2,published online 08 May 2025 In this article the value “0.250” of Table 1 was incorrectly written as “250”. The original article has been corrected .
文摘Nicolas Roisin,Guillaume Brunin,Gian-Marco Rignanese,Denis Flandre,Jean-Pierre Raskin&Samuel PoncéCorrection to: npj Computational Materials https://doi.org/10.1038/s41524-024-01425-0, published online 12 October 2024In this article, references 9, 11, 18-21, 34, 41, 44, 45, 47, 49, 56 and 62 were incomplete in the reference list. which have now beenupdated, and the original article has been corrected.
