Solid-state batteries are widely recognized as the next-generation energy storage devices with high specific energy,high safety,and high environmental adaptability.However,the research and development of solid-state b...Solid-state batteries are widely recognized as the next-generation energy storage devices with high specific energy,high safety,and high environmental adaptability.However,the research and development of solid-state batteries are resource-intensive and time-consuming due to their complex chemical environment,rendering performance prediction arduous and delaying large-scale industrialization.Artificial intelligence serves as an accelerator for solid-state battery development by enabling efficient material screening and performance prediction.This review will systematically examine how the latest progress in using machine learning(ML)algorithms can be used to mine extensive material databases and accelerate the discovery of high-performance cathode,anode,and electrolyte materials suitable for solid-state batteries.Furthermore,the use of ML technology to accurately estimate and predict key performance indicators in the solid-state battery management system will be discussed,among which are state of charge,state of health,remaining useful life,and battery capacity.Finally,we will summarize the main challenges encountered in the current research,such as data quality issues and poor code portability,and propose possible solutions and development paths.These will provide clear guidance for future research and technological reiteration.展开更多
In experiments searching for rare signals,background events from the detector itself are some of the major factors limiting search sensitivity.Screening for ultra-low radioactive detector materials is becoming ever mo...In experiments searching for rare signals,background events from the detector itself are some of the major factors limiting search sensitivity.Screening for ultra-low radioactive detector materials is becoming ever more essential.We propose to develop a gaseous time projection chamber(TPC)with a Micromegas readout for radio screening.The TPC records three-dimensional trajectories of charged particles emitted from a flat sample placed in the active volume of the detector.The detector can distinguish the origin of an event and identify the particle types with information from trajectories,which significantly increases the screening sensitivity.For a particles from the sample surface,we observe that our proposed detector can reach a sensitivity higher than 100 l Bq m-2 within two days.展开更多
The reaction characteristics of calcium-based materials during calcium looping(CaL)process are pivotal in the efficiency of CaL thermochemical energy storage(TCES)and CO_(2)capture systems.Currently,metal oxide doping...The reaction characteristics of calcium-based materials during calcium looping(CaL)process are pivotal in the efficiency of CaL thermochemical energy storage(TCES)and CO_(2)capture systems.Currently,metal oxide doping is the primary method to enhance the reaction characteristics of calcium-based materials over multiple cycles.In particular,co-doping with variable-valence metal oxides(VVMOs)can effectively increase the oxygen vacancy content in calcium-based materials,significantly improving their cyclic reaction characteristics.However,there are so numerous VVMOs co-doping schemes that the experimental screening process is complex,consuming considerable time and economic costs.Density functional theory(DFT)calculations have been widely used to reveal the impact of metal oxide doping on the cyclic reaction characteristics of calcium-based materials,with calculation results showing good agreement with experimental conclusions.Nevertheless,there is still a lack of research on utilizing DFT to screen calcium-based materials,and a systematic research methodology has not yet been established.In this study,a systematic DFT-based screening methodology for calcium-based materials was proposed.A series of key parameters for DFT calculations including CO_(2)adsorption energy,oxygen vacancy formation energy,and sintering resistance were proposed.Furthermore,a preliminary mathematical model to predict the CaL TCES and CO_(2)capture performance of calcium-based materials was introduced.The aforementioned DFT method was employed to screen for VVMOs co-doped calcium-based materials.The results revealed that Mn and Ce co-doped calcium-based materials exhibited superior DFT-predicted reaction characteristics.These DFT predictions were validated through experimental assessments of cyclic thermochemical energy storage,CO_(2)capture,and relevant characterization.The outcomes demonstrate a high degree of consistency among DFT-based predictions,experimental results,and characterization.Hence,the DFT-based screening methodology for calcium-based materials proposed herein is a viable solution,poised to offer theoretical insights for the efficient design of calcium-based materials.展开更多
As energy demands continue to rise in modern society,the development of high-performance lithium-ion batteries(LIBs)has become crucial.However,traditional research methods of material science face challenges such as l...As energy demands continue to rise in modern society,the development of high-performance lithium-ion batteries(LIBs)has become crucial.However,traditional research methods of material science face challenges such as lengthy timelines and complex processes.In recent years,the integration of machine learning(ML)in LIB materials,including electrolytes,solid-state electrolytes,and electrodes,has yielded remarkable achievements.This comprehensive review explores the latest applications of ML in predicting LIB material performance,covering the core principles and recent advancements in three key inverse material design strategies:high-throughput virtual screening,global optimization,and generative models.These strategies have played a pivotal role in fostering LIB material innovations.Meanwhile,the paper briefly discusses the challenges associated with applying ML to materials research and offers insights and directions for future research.展开更多
Magnesium-ion batteries hold promise as future energy storage solutions,yet current Mg cathodes are challenged by low voltage and specific capacity.Herein,we present an AI-driven workflow for discovering high-performa...Magnesium-ion batteries hold promise as future energy storage solutions,yet current Mg cathodes are challenged by low voltage and specific capacity.Herein,we present an AI-driven workflow for discovering high-performance Mg cathode materials.Utilizing the common characteristics of various ionic intercalation-type electrodes,we design and train a Crystal Graph Convolutional Neural Network model that can accurately predict electrode voltages for various ions with mean absolute errors(MAE)between0.25 and 0.33 V.By deploying the trained model to stable Mg compounds from Materials Project and GNoME AI dataset,we identify 160 high voltage structures out of 15,308 candidates with voltages above3.0 V and volumetric capacity over 800 mA h/cm^(3).We further train a precise NequIP model to facilitate accurate and rapid simulations of Mg ionic conductivity.From the 160 high voltage structures,the machine learning molecular dynamics simulations have selected 23 cathode materials with both high energy density and high ionic conductivity.This Al-driven workflow dramatically boosts the efficiency and precision of material discovery for multivalent ion batteries,paving the way for advanced Mg battery development.展开更多
Materials development has historically been driven by human needs and desires, and this is likely to con- tinue in the foreseeable future. The global population is expected to reach ten billion by 2050, which will pro...Materials development has historically been driven by human needs and desires, and this is likely to con- tinue in the foreseeable future. The global population is expected to reach ten billion by 2050, which will promote increasingly large demands for clean and high-ef ciency energy, personalized consumer prod- ucts, secure food supplies, and professional healthcare. New functional materials that are made and tai- lored for targeted properties or behaviors will be the key to tackling this challenge. Traditionally, advanced materials are found empirically or through experimental trial-and-error approaches. As big data generated by modern experimental and computational techniques is becoming more readily avail- able, data-driven or machine learning (ML) methods have opened new paradigms for the discovery and rational design of materials. In this review article, we provide a brief introduction on various ML methods and related software or tools. Main ideas and basic procedures for employing ML approaches in materials research are highlighted. We then summarize recent important applications of ML for the large-scale screening and optimal design of polymer and porous materials, catalytic materials, and energetic mate- rials. Finally, concluding remarks and an outlook are provided.展开更多
A large database is desired for machine learning(ML) technology to make accurate predictions of materials physicochemical properties based on their molecular structure.When a large database is not available,the develo...A large database is desired for machine learning(ML) technology to make accurate predictions of materials physicochemical properties based on their molecular structure.When a large database is not available,the development of proper featurization method based on physicochemical nature of target proprieties can improve the predictive power of ML models with a smaller database.In this work,we show that two new featurization methods,volume occupation spatial matrix and heat contribution spatial matrix,can improve the accuracy in predicting energetic materials' crystal density(ρ_(crystal)) and solid phase enthalpy of formation(H_(f,solid)) using a database containing 451 energetic molecules.Their mean absolute errors are reduced from 0.048 g/cm~3 and 24.67 kcal/mol to 0.035 g/cm~3 and 9.66 kcal/mol,respectively.By leave-one-out-cross-validation,the newly developed ML models can be used to determine the performance of most kinds of energetic materials except cubanes.Our ML models are applied to predict ρ_(crystal) and H_(f,solid) of CHON-based molecules of the 150 million sized PubChem database,and screened out 56 candidates with competitive detonation performance and reasonable chemical structures.With further improvement in future,spatial matrices have the potential of becoming multifunctional ML simulation tools that could provide even better predictions in wider fields of materials science.展开更多
Artificial intelligence(AI)is profoundly reshaping the discovery and design of organic light-emitting diode(OLED)materials,shifting conventional intuition-driven development into an integrated,datadriven paradigm.The ...Artificial intelligence(AI)is profoundly reshaping the discovery and design of organic light-emitting diode(OLED)materials,shifting conventional intuition-driven development into an integrated,datadriven paradigm.The increasing demand for high-performance OLED emitters with ultra-narrow emission spectrum and enhanced operational stability has highlighted the urgent need for a dedicated,multi-scale computational framework tailored to OLED-specific challenges.This review proposes a systematic AI-driven framework that combines quantum chemistry calculations,property prediction models,and generative algorithms to enable high-throughput screening and inverse design workflows for organic luminescent materials.Each component is critically analyzed in terms of theoretical underpinnings,practical benefits,inherent limitations,and avenues for further optimization.By presenting detailed case studies,we elucidate how AI approaches can tackle key bottlenecks in OLED material discovery and development.Moreover,we highlight essential future directions,including the integration of domain-specific expertise,the establishment of high-quality experimentally validated datasets,and the creation of molecular generation models specifically adapted for luminescent materials.Overall,this review aims to provide a comprehensive roadmap for advancing AI-guided materials research,offering transferable insights that extend beyond OLEDs to a broad range of organic optoelectronic materials.展开更多
Predicting the material stability is essential for accelerating the discovery of advanced materials in renewable energy, aerospace, and catalysis. Traditional approaches, such as Density Functional Theory (DFT), are a...Predicting the material stability is essential for accelerating the discovery of advanced materials in renewable energy, aerospace, and catalysis. Traditional approaches, such as Density Functional Theory (DFT), are accurate but computationally expensive and unsuitable for high-throughput screening. This study introduces a machine learning (ML) framework trained on high-dimensional data from the Open Quantum Materials Database (OQMD) to predict formation energy, a key stability metric. Among the evaluated models, deep learning outperformed Gradient Boosting Machines and Random Forest, achieving up to 0.88 R2 prediction accuracy. Feature importance analysis identified thermodynamic, electronic, and structural properties as the primary drivers of stability, offering interpretable insights into material behavior. Compared to DFT, the proposed ML framework significantly reduces computational costs, enabling the rapid screening of thousands of compounds. These results highlight ML’s transformative potential in materials discovery, with direct applications in energy storage, semiconductors, and catalysis.展开更多
Inorganic solid electrolytes have distinguished advantages in terms of safety and stability, and are promising to substitute for conventional organic liquid electrolytes. However, low ionic conductivity of typical can...Inorganic solid electrolytes have distinguished advantages in terms of safety and stability, and are promising to substitute for conventional organic liquid electrolytes. However, low ionic conductivity of typical candidates is the key problem. As connective diffusion path is the prerequisite for high performance, we screen for possible solid electrolytes from the 2004 International Centre for Diffraction Data （ICDD） database by calculating conduction pathways using Bond Valence （BV） method. There are 109846 inorganic crystals in the 2004 ICDD database, and 5295 of them contain lithium. Except for those with toxic, radioactive, rare, or variable valence elements, 1380 materials are candidates for solid electrolytes. The rationality of the BV method is approved by comparing the existing solid electrolytes＇ conduction pathways we had calculated with those from ex- periments or first principle calculations. The implication for doping and substitution, two important ways to improve the conductivity, is also discussed. Among them LizCO3 is selected for a detailed comparison, and the pathway is reproduced well with that based on the density functional studies. To reveal the correlation between connectivity of pathways and conductivity, a/γ-LiAlO2 and Li2CO3 are investigated by the impedance spectrum as an example, and many experimental and theoretical studies are in process to indicate the relationship between property and structure. The BV method can calculate one material within a few minutes, providing an efficient way to lock onto targets from abundant data, and to investigate the struc- ture-property relationship systematically.展开更多
Functional materials are widely used in chemical industry in order to reduce the process cost while simultaneously increase the product quality.Considering their significant effects,systematic methods for the optimal ...Functional materials are widely used in chemical industry in order to reduce the process cost while simultaneously increase the product quality.Considering their significant effects,systematic methods for the optimal selection and design of materials are essential.The conventional synthesis-and-test method for materials development is inefficient and costly.Additionally,the performance of the resulting materials is usually limited by the designer’s expertise.During the past few decades,computational methods have been significantly developed and they now become a very important tool for the optimal design of functional materials for various chemical processes.This article selectively focuses on two important process functional materials,namely heterogeneous catalyst and gas separation agent.Theoretical methods and representative works for computational screening and design of these materials are reviewed.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
基金the National Key Research Program of China under granted No.92164201National Natural Science Foundation of China for Distinguished Young Scholars No.62325403+2 种基金Natural Science Foundation of Jiangsu Province(BK20230498)Jiangsu Funding Program for Excellent Postdoctoral Talent(2024ZB427)the National Natural Science Foundation of China(62304147).
文摘Solid-state batteries are widely recognized as the next-generation energy storage devices with high specific energy,high safety,and high environmental adaptability.However,the research and development of solid-state batteries are resource-intensive and time-consuming due to their complex chemical environment,rendering performance prediction arduous and delaying large-scale industrialization.Artificial intelligence serves as an accelerator for solid-state battery development by enabling efficient material screening and performance prediction.This review will systematically examine how the latest progress in using machine learning(ML)algorithms can be used to mine extensive material databases and accelerate the discovery of high-performance cathode,anode,and electrolyte materials suitable for solid-state batteries.Furthermore,the use of ML technology to accurately estimate and predict key performance indicators in the solid-state battery management system will be discussed,among which are state of charge,state of health,remaining useful life,and battery capacity.Finally,we will summarize the main challenges encountered in the current research,such as data quality issues and poor code portability,and propose possible solutions and development paths.These will provide clear guidance for future research and technological reiteration.
基金the Ministry of Science and Technology of China(No.2016YFA0400302)the National Natural Sciences Foundation of China(Nos.11775142 and U1965201)the Chinese Academy of Sciences Center for Excellence in Particle Physics(CCEPP).
文摘In experiments searching for rare signals,background events from the detector itself are some of the major factors limiting search sensitivity.Screening for ultra-low radioactive detector materials is becoming ever more essential.We propose to develop a gaseous time projection chamber(TPC)with a Micromegas readout for radio screening.The TPC records three-dimensional trajectories of charged particles emitted from a flat sample placed in the active volume of the detector.The detector can distinguish the origin of an event and identify the particle types with information from trajectories,which significantly increases the screening sensitivity.For a particles from the sample surface,we observe that our proposed detector can reach a sensitivity higher than 100 l Bq m-2 within two days.
基金supported by the National Natural Science Foundation of China(52276204 and U22A20435)。
文摘The reaction characteristics of calcium-based materials during calcium looping(CaL)process are pivotal in the efficiency of CaL thermochemical energy storage(TCES)and CO_(2)capture systems.Currently,metal oxide doping is the primary method to enhance the reaction characteristics of calcium-based materials over multiple cycles.In particular,co-doping with variable-valence metal oxides(VVMOs)can effectively increase the oxygen vacancy content in calcium-based materials,significantly improving their cyclic reaction characteristics.However,there are so numerous VVMOs co-doping schemes that the experimental screening process is complex,consuming considerable time and economic costs.Density functional theory(DFT)calculations have been widely used to reveal the impact of metal oxide doping on the cyclic reaction characteristics of calcium-based materials,with calculation results showing good agreement with experimental conclusions.Nevertheless,there is still a lack of research on utilizing DFT to screen calcium-based materials,and a systematic research methodology has not yet been established.In this study,a systematic DFT-based screening methodology for calcium-based materials was proposed.A series of key parameters for DFT calculations including CO_(2)adsorption energy,oxygen vacancy formation energy,and sintering resistance were proposed.Furthermore,a preliminary mathematical model to predict the CaL TCES and CO_(2)capture performance of calcium-based materials was introduced.The aforementioned DFT method was employed to screen for VVMOs co-doped calcium-based materials.The results revealed that Mn and Ce co-doped calcium-based materials exhibited superior DFT-predicted reaction characteristics.These DFT predictions were validated through experimental assessments of cyclic thermochemical energy storage,CO_(2)capture,and relevant characterization.The outcomes demonstrate a high degree of consistency among DFT-based predictions,experimental results,and characterization.Hence,the DFT-based screening methodology for calcium-based materials proposed herein is a viable solution,poised to offer theoretical insights for the efficient design of calcium-based materials.
基金supported by the National Natural Science Foundation of China(Grant Nos.22225801,W2441009,22408228)。
文摘As energy demands continue to rise in modern society,the development of high-performance lithium-ion batteries(LIBs)has become crucial.However,traditional research methods of material science face challenges such as lengthy timelines and complex processes.In recent years,the integration of machine learning(ML)in LIB materials,including electrolytes,solid-state electrolytes,and electrodes,has yielded remarkable achievements.This comprehensive review explores the latest applications of ML in predicting LIB material performance,covering the core principles and recent advancements in three key inverse material design strategies:high-throughput virtual screening,global optimization,and generative models.These strategies have played a pivotal role in fostering LIB material innovations.Meanwhile,the paper briefly discusses the challenges associated with applying ML to materials research and offers insights and directions for future research.
基金supported by the National Key R&D Program of China(2022YFA1203400)the National Natural Science Foundation of China(W2441009)。
文摘Magnesium-ion batteries hold promise as future energy storage solutions,yet current Mg cathodes are challenged by low voltage and specific capacity.Herein,we present an AI-driven workflow for discovering high-performance Mg cathode materials.Utilizing the common characteristics of various ionic intercalation-type electrodes,we design and train a Crystal Graph Convolutional Neural Network model that can accurately predict electrode voltages for various ions with mean absolute errors(MAE)between0.25 and 0.33 V.By deploying the trained model to stable Mg compounds from Materials Project and GNoME AI dataset,we identify 160 high voltage structures out of 15,308 candidates with voltages above3.0 V and volumetric capacity over 800 mA h/cm^(3).We further train a precise NequIP model to facilitate accurate and rapid simulations of Mg ionic conductivity.From the 160 high voltage structures,the machine learning molecular dynamics simulations have selected 23 cathode materials with both high energy density and high ionic conductivity.This Al-driven workflow dramatically boosts the efficiency and precision of material discovery for multivalent ion batteries,paving the way for advanced Mg battery development.
文摘Materials development has historically been driven by human needs and desires, and this is likely to con- tinue in the foreseeable future. The global population is expected to reach ten billion by 2050, which will promote increasingly large demands for clean and high-ef ciency energy, personalized consumer prod- ucts, secure food supplies, and professional healthcare. New functional materials that are made and tai- lored for targeted properties or behaviors will be the key to tackling this challenge. Traditionally, advanced materials are found empirically or through experimental trial-and-error approaches. As big data generated by modern experimental and computational techniques is becoming more readily avail- able, data-driven or machine learning (ML) methods have opened new paradigms for the discovery and rational design of materials. In this review article, we provide a brief introduction on various ML methods and related software or tools. Main ideas and basic procedures for employing ML approaches in materials research are highlighted. We then summarize recent important applications of ML for the large-scale screening and optimal design of polymer and porous materials, catalytic materials, and energetic mate- rials. Finally, concluding remarks and an outlook are provided.
基金support from the Ministry of Education(MOE) Singapore Tier 1 (RG8/20)。
文摘A large database is desired for machine learning(ML) technology to make accurate predictions of materials physicochemical properties based on their molecular structure.When a large database is not available,the development of proper featurization method based on physicochemical nature of target proprieties can improve the predictive power of ML models with a smaller database.In this work,we show that two new featurization methods,volume occupation spatial matrix and heat contribution spatial matrix,can improve the accuracy in predicting energetic materials' crystal density(ρ_(crystal)) and solid phase enthalpy of formation(H_(f,solid)) using a database containing 451 energetic molecules.Their mean absolute errors are reduced from 0.048 g/cm~3 and 24.67 kcal/mol to 0.035 g/cm~3 and 9.66 kcal/mol,respectively.By leave-one-out-cross-validation,the newly developed ML models can be used to determine the performance of most kinds of energetic materials except cubanes.Our ML models are applied to predict ρ_(crystal) and H_(f,solid) of CHON-based molecules of the 150 million sized PubChem database,and screened out 56 candidates with competitive detonation performance and reasonable chemical structures.With further improvement in future,spatial matrices have the potential of becoming multifunctional ML simulation tools that could provide even better predictions in wider fields of materials science.
基金supported by the Beijing Natural Science Foundation(2242054)the National Natural Science Foundation of China(62075006 and 62475177)。
文摘Artificial intelligence(AI)is profoundly reshaping the discovery and design of organic light-emitting diode(OLED)materials,shifting conventional intuition-driven development into an integrated,datadriven paradigm.The increasing demand for high-performance OLED emitters with ultra-narrow emission spectrum and enhanced operational stability has highlighted the urgent need for a dedicated,multi-scale computational framework tailored to OLED-specific challenges.This review proposes a systematic AI-driven framework that combines quantum chemistry calculations,property prediction models,and generative algorithms to enable high-throughput screening and inverse design workflows for organic luminescent materials.Each component is critically analyzed in terms of theoretical underpinnings,practical benefits,inherent limitations,and avenues for further optimization.By presenting detailed case studies,we elucidate how AI approaches can tackle key bottlenecks in OLED material discovery and development.Moreover,we highlight essential future directions,including the integration of domain-specific expertise,the establishment of high-quality experimentally validated datasets,and the creation of molecular generation models specifically adapted for luminescent materials.Overall,this review aims to provide a comprehensive roadmap for advancing AI-guided materials research,offering transferable insights that extend beyond OLEDs to a broad range of organic optoelectronic materials.
文摘Predicting the material stability is essential for accelerating the discovery of advanced materials in renewable energy, aerospace, and catalysis. Traditional approaches, such as Density Functional Theory (DFT), are accurate but computationally expensive and unsuitable for high-throughput screening. This study introduces a machine learning (ML) framework trained on high-dimensional data from the Open Quantum Materials Database (OQMD) to predict formation energy, a key stability metric. Among the evaluated models, deep learning outperformed Gradient Boosting Machines and Random Forest, achieving up to 0.88 R2 prediction accuracy. Feature importance analysis identified thermodynamic, electronic, and structural properties as the primary drivers of stability, offering interpretable insights into material behavior. Compared to DFT, the proposed ML framework significantly reduces computational costs, enabling the rapid screening of thousands of compounds. These results highlight ML’s transformative potential in materials discovery, with direct applications in energy storage, semiconductors, and catalysis.
基金supported by the National Natural Science Foundation of China (Grant Nos. 11234013 and 51172274)the "Strategic Priority Research Program" of the Chinese Academy of Sciences (Grant No. XDA01010202)+1 种基金the National Basic Research Program of China (Grant No. 2012CB932900)the Project of Beijing Municipal Science & Technology Commission
文摘Inorganic solid electrolytes have distinguished advantages in terms of safety and stability, and are promising to substitute for conventional organic liquid electrolytes. However, low ionic conductivity of typical candidates is the key problem. As connective diffusion path is the prerequisite for high performance, we screen for possible solid electrolytes from the 2004 International Centre for Diffraction Data （ICDD） database by calculating conduction pathways using Bond Valence （BV） method. There are 109846 inorganic crystals in the 2004 ICDD database, and 5295 of them contain lithium. Except for those with toxic, radioactive, rare, or variable valence elements, 1380 materials are candidates for solid electrolytes. The rationality of the BV method is approved by comparing the existing solid electrolytes＇ conduction pathways we had calculated with those from ex- periments or first principle calculations. The implication for doping and substitution, two important ways to improve the conductivity, is also discussed. Among them LizCO3 is selected for a detailed comparison, and the pathway is reproduced well with that based on the density functional studies. To reveal the correlation between connectivity of pathways and conductivity, a/γ-LiAlO2 and Li2CO3 are investigated by the impedance spectrum as an example, and many experimental and theoretical studies are in process to indicate the relationship between property and structure. The BV method can calculate one material within a few minutes, providing an efficient way to lock onto targets from abundant data, and to investigate the struc- ture-property relationship systematically.
文摘Functional materials are widely used in chemical industry in order to reduce the process cost while simultaneously increase the product quality.Considering their significant effects,systematic methods for the optimal selection and design of materials are essential.The conventional synthesis-and-test method for materials development is inefficient and costly.Additionally,the performance of the resulting materials is usually limited by the designer’s expertise.During the past few decades,computational methods have been significantly developed and they now become a very important tool for the optimal design of functional materials for various chemical processes.This article selectively focuses on two important process functional materials,namely heterogeneous catalyst and gas separation agent.Theoretical methods and representative works for computational screening and design of these materials are reviewed.
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.
