The globally increasing concentrations of greenhouse gases in atmosphere after combustion of coal-or petroleum-based fuels give rise to tremendous interest in searching for porous materials to efficiently capture carb...The globally increasing concentrations of greenhouse gases in atmosphere after combustion of coal-or petroleum-based fuels give rise to tremendous interest in searching for porous materials to efficiently capture carbon dioxide(CO_2) and store methane(CH4), where the latter is a kind of clean energy source with abundant reserves and lower CO_2 emission. Hundreds of thousands of porous materials can be enrolled on the candidate list, but how to quickly identify the really promising ones, or even evolve materials(namely, rational design high-performing candidates) based on the large database of present porous materials? In this context, high-throughput computational techniques, which have emerged in the past few years as powerful tools, make the targets of fast evaluation of adsorbents and evolving materials for CO_2 capture and CH_4 storage feasible. This review provides an overview of the recent computational efforts on such related topics and discusses the further development in this field.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
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
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
Developing new responsive materials whose physico-chemical properties can be controlled and tailored by external stimuli is fundamental for many modern technologies.In this framework,3D-printable photochromic material...Developing new responsive materials whose physico-chemical properties can be controlled and tailored by external stimuli is fundamental for many modern technologies.In this framework,3D-printable photochromic materials and systems for all-optical data processing might enable remote addressing,by optical control of their response with high spatiotemporal accuracy,thus supporting the development of new computing and sensing platforms with multidimensional fashion.Here,we introduce 3D-printable photochromic materials based on either a spiropyran molecular system or a diarylethene derivative shaped by digital light processing.Dynamically controlling transmitted light by the intensity and sequence of incoming signals,these materials exhibit robust photoswitching cycles,long-term optically-textured information storage,and are used in 3D printed devices capable of all-optical arithmetic and logic processing.These compounds and devices open a route to new 3D all-organic all-optical computing platforms,and to new schemes and architectures for advanced microscopy,sensing,and physical intelligence.展开更多
基金supported by the Natural Science Foundation of China (Nos.21706106,21536001 and 21322603)the National Key Basic Research Program of China ("973") (No.2013CB733503)+1 种基金the Natural Science Foundation of Jiangsu Normal University(16XLR011)Priority Academic Program Development of Jiangsu Higher Education Institutions
文摘The globally increasing concentrations of greenhouse gases in atmosphere after combustion of coal-or petroleum-based fuels give rise to tremendous interest in searching for porous materials to efficiently capture carbon dioxide(CO_2) and store methane(CH4), where the latter is a kind of clean energy source with abundant reserves and lower CO_2 emission. Hundreds of thousands of porous materials can be enrolled on the candidate list, but how to quickly identify the really promising ones, or even evolve materials(namely, rational design high-performing candidates) based on the large database of present porous materials? In this context, high-throughput computational techniques, which have emerged in the past few years as powerful tools, make the targets of fast evaluation of adsorbents and evolving materials for CO_2 capture and CH_4 storage feasible. This review provides an overview of the recent computational efforts on such related topics and discusses the further development in this field.
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.
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.
基金funding from the European Research Council(ERC)under the European Union’s Horizon 2020 research and innovation program(grant agreement No.682157,“xPRINT”)the Italian Minister of University and Research PRIN 2017PHRM8X project(“3D-Phys”)funding from the project“MAD-La metamorfosi Additiva del Design”(PON‘Ricerca e Innovazione 2014-2020,ARS01_00717).
文摘Developing new responsive materials whose physico-chemical properties can be controlled and tailored by external stimuli is fundamental for many modern technologies.In this framework,3D-printable photochromic materials and systems for all-optical data processing might enable remote addressing,by optical control of their response with high spatiotemporal accuracy,thus supporting the development of new computing and sensing platforms with multidimensional fashion.Here,we introduce 3D-printable photochromic materials based on either a spiropyran molecular system or a diarylethene derivative shaped by digital light processing.Dynamically controlling transmitted light by the intensity and sequence of incoming signals,these materials exhibit robust photoswitching cycles,long-term optically-textured information storage,and are used in 3D printed devices capable of all-optical arithmetic and logic processing.These compounds and devices open a route to new 3D all-organic all-optical computing platforms,and to new schemes and architectures for advanced microscopy,sensing,and physical intelligence.
基金financially supported by the National Key Research and Development Program of China(2021YFB3601502)the Key Research Program of Frontier Sciences,CAS(ZDBS-LY-SLH035)+6 种基金the National Natural Science Foundation of China(22193044,61835014,51972336)the West Light Foundation of CAS(2019-YDYLTD-002)the Natural Science Foundation of Xinjiang(2021D01E05)the CAS Project for Young Scientists in Basic Research(YSBR-024)Xinjiang Major Science and Technology Project(2021A01001)the CAS President’s International Fellowship Initiative(PIFI,2020PM0046)Tianshan Basic Research Talents(2022TSYCJU0001)。
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)+4 种基金the Key Research Project of Zhejiang Laboratory(2021PE0AC02)the support from Guangdong Innovation Research Team Project(2017ZT07C062)Guangdong Provincial Key-Lab program(2019B030301001)Shenzhen Municipal Key-Lab program(ZDSYS20190902092905285)supported by the Center for Computational Science and Engineering at Southern University of Science and Technology。
