MatCloud provides a high-throughput computational materials infrastructure for the integrated management of materials simulation, data, and computing resources. In comparison to AFLOW, Material Project, and NoMad, Mat...MatCloud provides a high-throughput computational materials infrastructure for the integrated management of materials simulation, data, and computing resources. In comparison to AFLOW, Material Project, and NoMad, MatCloud delivers two-fold functionalities： a computational materials platform where users can do on-line job setup, job submission and monitoring only via Web browser, and a materials properties simulation database. It is developed under Chinese Materials Genome Initiative and is a China own proprietary high-throughput computational materials infrastructure. MatCloud has been on line for about one year, receiving considerable registered users, feedbacks, and encouragements. Many users provided valuable input and requirements to MatCloud. In this paper, we describe the present MatCloud, future visions, and major challenges. Based on what we have achieved, we will endeavour to further develop MatCloud in an open and collaborative manner and make MatCloud a world known China-developed novel software in the pressing area of high-throughput materials calculations and materials properties simulation database within Material Genome Initiative.展开更多
This paper reviews the rapid progress in the field of high-throughput modeling based on the Materials Genome Initiative, and its application in the discovery and design of lithium battery materials. It offers examples...This paper reviews the rapid progress in the field of high-throughput modeling based on the Materials Genome Initiative, and its application in the discovery and design of lithium battery materials. It offers examples of screening, optimization and design of electrodes, electrolytes, coatings, additives, etc. and the possibility of introducing the machine learning method into material design. The application of the material genome method in the development of lithium battery materials provides the possibility to speed up the upgrading of new candidates in the discovery of lots of functional materials.展开更多
The development of new engineering alloy chemistries and heat treatments is a time-consuming and iterative process.Here,a hybrid approach of the high-throughput precipitation simulations and decisive experiments is de...The development of new engineering alloy chemistries and heat treatments is a time-consuming and iterative process.Here,a hybrid approach of the high-throughput precipitation simulations and decisive experiments is developed to optimize the composition and manipulate the microstructure of Al-Zn-Mg-Cu alloys to achieve the expected yield strength and elongation.For that purpose,a multi-class Kampmann-Wagner numerical(KWN)framework is established and the contributions to precipitation kinetics and strength from primary phases and precipitates formed before age hardening are introduced for the first time.The composition/process-structure-property relationship of Al-Zn-Mg-Cu alloys is pre-sented and discussed in detail.Coupled with thermodynamic calculations,two concentration-optimized Al-Zn-Mg-Cu alloys with expected high yield strength and long elongation are designed,prepared,and characterized.The excellent strength and elongation of the designed alloys and the good agreement between the measured and model-predicted mechanical properties for these two alloys underscores the remarkable predictive power of the presently developed material design strategy.This work establishes a novel material design strategy for rapidly exploring the compositional space and investigating the effects of composition and heat treatment on the microstructure and performance of ultrahigh strength Al alloys and other materials.展开更多
Japanese IMS-VHT project on the Virtual Heat Treatment tool for monitoring and optimising HT process in relation with the international cooperative programs is briefly introduced. This project motivates to develop vir...Japanese IMS-VHT project on the Virtual Heat Treatment tool for monitoring and optimising HT process in relation with the international cooperative programs is briefly introduced. This project motivates to develop virtual tools for computer to optimize the heat treatment condition and to support decision for HT operation by knowledge based database in addition to process simulation. As one of the activities with the cooperation of the Society of Materials Science, Japan and the Japan Society for Heat Treatment, a benchmark project is undergoing. This includes simulation of carburized quenching process of a cylinder, disc, and ring as well as a helical gear by use of common data of materials properties and cooling characteristics by several available simulation programs. A part of the newly obtained results is presented as an interim report.展开更多
Fischer-Tropsch synthesis is an important method for producing clean fuels and fine chemicals,but by-products such as CO_(2)bring severe challenges of low energy utilization and air pollution in commercial-scale produ...Fischer-Tropsch synthesis is an important method for producing clean fuels and fine chemicals,but by-products such as CO_(2)bring severe challenges of low energy utilization and air pollution in commercial-scale production.In this work,the competitive adsorption selectivity of CO_(2)in a five-component gas mixture of tens of thousands of porous materials was calculated based on high-throughput screening and grand canonical Monte Carlo simulation.Seven promising CO_(2)-type adsorbents were obtained under equimolar and industrial components,among which RUBTAK03 had a higher adsorption selectivity between 65 and 75.The CO_(2)adsorption capacity of KINNIG under a single component was 8.72 mmol/g at 298 K and 1 bar,surpassing most well-known metal–organic frameworks.This strong CO_(2)capture performance originates from three-dimensional interlaced channels,fluorinated organic ligands,and ultra-micropores,including channels and cages.In particular,this type of porous material composed of organic ligands or inorganic pillars containing fluorine atoms achieves an efficient capture of CO_(2)from air and industrial tail gas,providing theoretical guidance for the design of novel and efficient adsorbents.展开更多
Heterogeneous catalysis remains at the core of various bulk chemical manufacturing and energy conversion processes,and its revolution necessitates the hunt for new materials with ideal catalytic activities and economi...Heterogeneous catalysis remains at the core of various bulk chemical manufacturing and energy conversion processes,and its revolution necessitates the hunt for new materials with ideal catalytic activities and economic feasibility.Computational high-throughput screening presents a viable solution to this challenge,as machine learning(ML)has demonstrated its great potential in accelerating such processes by providing satisfactory estimations of surface reactivity with relatively low-cost information.This review focuses on recent progress in applying ML in adsorption energy prediction,which predominantly quantifies the catalytic potential of a solid catalyst.ML models that leverage inputs from different categories and exhibit various levels of complexity are classified and discussed.At the end of the review,an outlook on the current challenges and future opportunities of ML-assisted catalyst screening is supplied.We believe that this review summarizes major achievements in accelerating catalyst discovery through ML and can inspire researchers to further devise novel strategies to accelerate materials design and,ultimately,reshape the chemical industry and energy landscape.展开更多
The fundamental idea of materials genome initiative is the integration of computing platform,experimental platform,and data platform to speed up the material innovation hence reduce time and cost.This paper describes ...The fundamental idea of materials genome initiative is the integration of computing platform,experimental platform,and data platform to speed up the material innovation hence reduce time and cost.This paper describes the basic concept of building an integrated computational platform and data platform for material innovation from the perspective of high-throughput simulation and materials knowledge management.The material data platform that can integrate material database,heterogeneous material data,various scripts,and open-source material simulation code together is particularly discussed.Taking metallic materials as an example,a brief introduction to metallic materials data management is given,and how to manage the semi-structure and unstructured iron and steel material data is also presented.展开更多
Spin Hall effect(SHE)provides a promising solution to the realization of advantageous functionalities for spin-based recording and information processing.In this work,we conduct high-throughput calculations on the spi...Spin Hall effect(SHE)provides a promising solution to the realization of advantageous functionalities for spin-based recording and information processing.In this work,we conduct high-throughput calculations on the spin Hall conductivity(SHC)of antiperovskite compounds with the composition ZXM3,where Z is a nonmetal,X is a metal,and M is a platinum group metal.From an initial database over 4500 structures,we screen 295 structurally stable compounds and identify 24 compounds with intrinsic SHC exceeding 500(ℏ/e)(Ω^(⁻1)cm^(⁻1)).We reveal a strong dependence of SHC on spin-orbit coupling-induced energy splitting near the Fermi level.In addition,SHCs can be regulated through proper doping of electrons or holes.The present work establishes high-throughput database of SHC in antiperovskites which is crucial for designing future electric and spintronic devices.展开更多
Dynaform是专业化的、应用广泛的板材成形数值模拟软件,但其自带数据库中常用合金材料的数据缺乏,急需进行完善。以SQL Server 2008为数据库平台,基于Visual Studio2012中的C#语言开发了Dynaform材料数据库系统,实现了对材料数据的查询...Dynaform是专业化的、应用广泛的板材成形数值模拟软件,但其自带数据库中常用合金材料的数据缺乏,急需进行完善。以SQL Server 2008为数据库平台,基于Visual Studio2012中的C#语言开发了Dynaform材料数据库系统,实现了对材料数据的查询、添加、修改、删除、搜索、排序等功能;同时,基于Dynaform软件所用的材料模型创建了材料模版,编制程序使数据库中的材料参数导入到模板的相应位置,最终实现材料参数的模型化,使得材料数据库的零散参数能够直接集成输入到该软件中,为数据的提取和调用提供了便利。展开更多
基金Project supported by the National Key Research and Development Program of China(Grant Nos.2017YFB0701702 and 2016YFB0700501)the National Natural Science Foundation of China(Grant Nos.61472394 and 11534012)Science and Technology Department of Sichuan Province,China(Grant No.2017JZ0001)
文摘MatCloud provides a high-throughput computational materials infrastructure for the integrated management of materials simulation, data, and computing resources. In comparison to AFLOW, Material Project, and NoMad, MatCloud delivers two-fold functionalities： a computational materials platform where users can do on-line job setup, job submission and monitoring only via Web browser, and a materials properties simulation database. It is developed under Chinese Materials Genome Initiative and is a China own proprietary high-throughput computational materials infrastructure. MatCloud has been on line for about one year, receiving considerable registered users, feedbacks, and encouragements. Many users provided valuable input and requirements to MatCloud. In this paper, we describe the present MatCloud, future visions, and major challenges. Based on what we have achieved, we will endeavour to further develop MatCloud in an open and collaborative manner and make MatCloud a world known China-developed novel software in the pressing area of high-throughput materials calculations and materials properties simulation database within Material Genome Initiative.
基金Project supported by the National Natural Science Foundation of China(Grant No.51772321)the Beijing Science and Technology Project(Grant No.D171100005517001)+1 种基金the National Key Research and Development Plan(Grant No.2017YFB0701602)the Youth Innovation Promotion Association(Grant No.2016005)
文摘This paper reviews the rapid progress in the field of high-throughput modeling based on the Materials Genome Initiative, and its application in the discovery and design of lithium battery materials. It offers examples of screening, optimization and design of electrodes, electrolytes, coatings, additives, etc. and the possibility of introducing the machine learning method into material design. The application of the material genome method in the development of lithium battery materials provides the possibility to speed up the upgrading of new candidates in the discovery of lots of functional materials.
基金supported by the National Key Research and Development Program of China(No.2018YFB0704003)the Funds for International Cooperation and Exchange of the National Natural Science Foundation of China(No.51820105001).
文摘The development of new engineering alloy chemistries and heat treatments is a time-consuming and iterative process.Here,a hybrid approach of the high-throughput precipitation simulations and decisive experiments is developed to optimize the composition and manipulate the microstructure of Al-Zn-Mg-Cu alloys to achieve the expected yield strength and elongation.For that purpose,a multi-class Kampmann-Wagner numerical(KWN)framework is established and the contributions to precipitation kinetics and strength from primary phases and precipitates formed before age hardening are introduced for the first time.The composition/process-structure-property relationship of Al-Zn-Mg-Cu alloys is pre-sented and discussed in detail.Coupled with thermodynamic calculations,two concentration-optimized Al-Zn-Mg-Cu alloys with expected high yield strength and long elongation are designed,prepared,and characterized.The excellent strength and elongation of the designed alloys and the good agreement between the measured and model-predicted mechanical properties for these two alloys underscores the remarkable predictive power of the presently developed material design strategy.This work establishes a novel material design strategy for rapidly exploring the compositional space and investigating the effects of composition and heat treatment on the microstructure and performance of ultrahigh strength Al alloys and other materials.
文摘Japanese IMS-VHT project on the Virtual Heat Treatment tool for monitoring and optimising HT process in relation with the international cooperative programs is briefly introduced. This project motivates to develop virtual tools for computer to optimize the heat treatment condition and to support decision for HT operation by knowledge based database in addition to process simulation. As one of the activities with the cooperation of the Society of Materials Science, Japan and the Japan Society for Heat Treatment, a benchmark project is undergoing. This includes simulation of carburized quenching process of a cylinder, disc, and ring as well as a helical gear by use of common data of materials properties and cooling characteristics by several available simulation programs. A part of the newly obtained results is presented as an interim report.
基金supported by the National Natural Science Foundation of China(12174450 and 11874429)the National Talents Program of China,Science and Technology Innovation Program of Hunan Province(2024RC1013)+6 种基金the Key Project of Natural Science Foundation of Hunan Province(Grant No.2024JJ3029)the Hunan Provincial Key Research and Development Program(Grant No.2022WK2002)the Distinguished Youth Foundation(2020JJ2039)the Project of High-Level Talents Accumulation(2018RS3021)the Program of Hundreds of Talents of Hunan Province,State Key Laboratory of Powder Metallurgy,Start-up Funding and Innovation-Driven Plan(2019CX023)of Central South University,Postgraduate Scientific Research Innovation Project of Hunan Province(CX20230104 and CX20220252)the Youth Student Program of Hunan Provincial Natural Science Foundation(Grant No.2025JJ60853)Calculations were performed at High-Performance Computing facilities of Central South University.
文摘Fischer-Tropsch synthesis is an important method for producing clean fuels and fine chemicals,but by-products such as CO_(2)bring severe challenges of low energy utilization and air pollution in commercial-scale production.In this work,the competitive adsorption selectivity of CO_(2)in a five-component gas mixture of tens of thousands of porous materials was calculated based on high-throughput screening and grand canonical Monte Carlo simulation.Seven promising CO_(2)-type adsorbents were obtained under equimolar and industrial components,among which RUBTAK03 had a higher adsorption selectivity between 65 and 75.The CO_(2)adsorption capacity of KINNIG under a single component was 8.72 mmol/g at 298 K and 1 bar,surpassing most well-known metal–organic frameworks.This strong CO_(2)capture performance originates from three-dimensional interlaced channels,fluorinated organic ligands,and ultra-micropores,including channels and cages.In particular,this type of porous material composed of organic ligands or inorganic pillars containing fluorine atoms achieves an efficient capture of CO_(2)from air and industrial tail gas,providing theoretical guidance for the design of novel and efficient adsorbents.
基金supported by the National Natural Science Foundation of China(22109020 and 22109082).
文摘Heterogeneous catalysis remains at the core of various bulk chemical manufacturing and energy conversion processes,and its revolution necessitates the hunt for new materials with ideal catalytic activities and economic feasibility.Computational high-throughput screening presents a viable solution to this challenge,as machine learning(ML)has demonstrated its great potential in accelerating such processes by providing satisfactory estimations of surface reactivity with relatively low-cost information.This review focuses on recent progress in applying ML in adsorption energy prediction,which predominantly quantifies the catalytic potential of a solid catalyst.ML models that leverage inputs from different categories and exhibit various levels of complexity are classified and discussed.At the end of the review,an outlook on the current challenges and future opportunities of ML-assisted catalyst screening is supplied.We believe that this review summarizes major achievements in accelerating catalyst discovery through ML and can inspire researchers to further devise novel strategies to accelerate materials design and,ultimately,reshape the chemical industry and energy landscape.
基金partially supported by Hundred Talents Program of Chinese Academy of SciencesDirector Funding of Computer Network Information Center,Chinese Academy of Sciencesthe‘‘China Engineering Science and Technology Knowledge Center’’of Chinese Academy of Engineering for the support and funding on the metal material information platform
文摘The fundamental idea of materials genome initiative is the integration of computing platform,experimental platform,and data platform to speed up the material innovation hence reduce time and cost.This paper describes the basic concept of building an integrated computational platform and data platform for material innovation from the perspective of high-throughput simulation and materials knowledge management.The material data platform that can integrate material database,heterogeneous material data,various scripts,and open-source material simulation code together is particularly discussed.Taking metallic materials as an example,a brief introduction to metallic materials data management is given,and how to manage the semi-structure and unstructured iron and steel material data is also presented.
基金supported by the National Natural Science Foundation of China(Grants Nos.12174450 and 11874429)the National Talents Program of China,the Science and Technology Innovation Program of Hunan Province(Grant No.2024RC1013)+3 种基金the Key Project of Hunan Provincial Natural Science Foundation(Grant No.2024JJ3029)the Hunan Provincial Key Research and Development Program(Grant No.2022WK2002)the Distinguished Youth Foundation of Hunan Province(Grant No.2020JJ2039),the Project of High-Level Talents Accumulation of Hunan Province(Grant No.2018RS3021)Program of Hundreds of Talents of Hunan Province,the State Key Laboratory of Powder Metallurgy,Start-up Funding and Innovation-Driven Plan(Grant No.2019CX023)of Central South University,Postgraduate Scientific Research Innovation Project of Hunan Province(Grants No.CX20230104)。
文摘Spin Hall effect(SHE)provides a promising solution to the realization of advantageous functionalities for spin-based recording and information processing.In this work,we conduct high-throughput calculations on the spin Hall conductivity(SHC)of antiperovskite compounds with the composition ZXM3,where Z is a nonmetal,X is a metal,and M is a platinum group metal.From an initial database over 4500 structures,we screen 295 structurally stable compounds and identify 24 compounds with intrinsic SHC exceeding 500(ℏ/e)(Ω^(⁻1)cm^(⁻1)).We reveal a strong dependence of SHC on spin-orbit coupling-induced energy splitting near the Fermi level.In addition,SHCs can be regulated through proper doping of electrons or holes.The present work establishes high-throughput database of SHC in antiperovskites which is crucial for designing future electric and spintronic devices.
文摘Dynaform是专业化的、应用广泛的板材成形数值模拟软件,但其自带数据库中常用合金材料的数据缺乏,急需进行完善。以SQL Server 2008为数据库平台,基于Visual Studio2012中的C#语言开发了Dynaform材料数据库系统,实现了对材料数据的查询、添加、修改、删除、搜索、排序等功能;同时,基于Dynaform软件所用的材料模型创建了材料模版,编制程序使数据库中的材料参数导入到模板的相应位置,最终实现材料参数的模型化,使得材料数据库的零散参数能够直接集成输入到该软件中,为数据的提取和调用提供了便利。
