检索结果-南通市图书馆

High-throughput computational screening of oxide double perovskites for optoelectronic and photocatalysis applications

在线全文

学校读者我要写书评

暂无评论

Journal of Energy Chemistry 2021年第6期30卷 351-358,I0009页

作者： Xiaowei jiang wan-jian yin College of Energy Soochow Institute for Energy and Materials InnovationS(SIEMIS)Soochow UniversitySuzhou 215006JiangsuChina Key Laboratory of Advanced Carbon Materials and Wearable Energy Technologies of jiangsu Province Soochow UniversitySuzhou 215006JiangsuChina Key Lab of Advanced Optical Manufacturing Technologies of jiangsu Province&Key Lab of Modern Optical Technologies of Education Ministry of China Soochow UniversitySuzhou 215006JiangsuChina

Oxide double perovskites A2 B’B"O6 are a class of emerging materials in the fields of optoelectronics and *** to the chemical flexibilities of perovskite structures,there are multiple elemental combinations of cations A,B’,and B",which leading to tremendous *** this study,we comprehensively screened stable oxide double perovskite A2 B’B"O6 from a pool of 2,018 perovskite candidates using a high-throughput computational *** considering a tolerance factor(t)-octahedral factor(μ) phase diagram,138 candidates with Fm 3 m, P21/c,and R3 c phases were selected and systematically studied via first-principles calculations based on density functional *** screening procedure finally predicted the existence of 21 stable perovskites,and 14 among them have never been *** with existing experimental results demonstrates that the prediction accuracy for perovskite formability is approximately 90%.The predicted oxide double perovskites exhibit quasi-direct bandgaps ranging from 0 to 4.4 eV with a significantly small direct-indirect bandgap difference,balanced electron and hole effective masses,and strong optical *** newly predicted oxide double perovskites may enlarge the pool of material candidates for applications in optoelectronics and *** study provides a route for computational screening of novel perovskites for functional applications.

关键词： Oxide double perovskites First-principles calculations High-throughput Optoelectronics Photocatalysis

Harnessing data using symbolic regression methods for discovering novel paradigms in physics

在线全文

学校读者我要写书评

暂无评论

Science China(Physics,Mechanics & Astronomy) 2024年第6期67卷 1-11页

作者： jianyang Guo wan-jian yin College of Energy Soochow Institute for Energy and Materials Innovations(SIEMIS)Jiangsu Provincial Key Laboratory for Advanced Carbon Materials and Wearable Energy TechnologiesSoochow UniversitySuzhou 215006China Shanghai Qi Zhi Institute Shanghai 200232China Light Industry Institute of Electrochemical Power Sources Soochow UniversitySuzhou 215006China

In recent years,machine-learning methods have profoundly impacted research in the interdisciplinary fields of ***,most machine-learning models lack interpretability,and physicists doubt the credibility of their conclusions because they cannot be combined with prior physical ***,this review focuses on symbolic regression,which is an interpretable machine-learning ***,the relevant concepts of machine learning are introduced in conjunction with ***,we provide an overview of symbolic regression ***,the recent directions for the application of symbolic regression methods in different subfields of physics are outlined,and an overview of the ways in which the applications of symbolic regression have evolved in the realm of physics is *** major aim of this review is to introduce the basic principles of symbolic regression and explain its applications in the field of physics.

关键词： machine learning genetic programming symbolic regression

Defect calculations with quasiparticle correction: A revisited study of iodine defects in CH3NH3PbI3

在线全文

学校读者我要写书评

暂无评论

Chinese Physics B 2022年第1期31卷 511-515页

作者： Ling Li wan-jian yin College of Energy Soochow Institute for Energy and Materials Innovations(SIEMIS)and Jiangsu Provincial Key Laboratory for Advanced Carbon Materials and Wearable Energy TechnologiesSoochow UniversitySuzhou 215006China Light Industry Institute of Electrochemical Power Sources Soochow UniversitySuzhou 215006China Key Laboratory of Advanced Optical Manufacturing Technologies of jiangsu Province&Key Laboratory of Modern Optical Technologies of the Education Ministry of China Soochow UniversitySuzhou 215006China

Defect levels in semiconductor band gaps play a crucial role in functionalized semiconductors for practical applications in optoelectronics;however,first-principle defect calculations based on exchange-correlation functionals,such as local density approximation,grand gradient approximation(GGA),and hybrid functionals,either underestimate band gaps or misplace defect *** this study,we revisited iodine defects in CH_(3)NH_(3)PbI_(3) by combining the accuracy of total energy calculations of GGA and single-electron level calculation of the GW *** combined approach predicted neutral Im_(i) to be unstable and the transition level of Im_(i)(+1/-1)to be close to the valence band ***,Im_(i) may not be as detrimental as previously ***,Vm I may be unstable in the-1 charged state but could still be detrimental owing to the deep transition level of Vm I(+1/0).These results could facilitate the further understanding of the intrinsic point defect and defect passivation observed in CH_(3)NH_(3)PbI_(3).

关键词： quasiparticle correction defect calculation GW theory methylammonium lead iodide

Designing solar-cell absorber materials through computational high-throughput screening

在线全文

学校读者我要写书评

暂无评论

Chinese Physics B 2020年第2期29卷 1-9页

作者： Xiaowei jiang wan-jian yin College of Energy Soochow Institute for Energy and Materials Innovations(SIEMIS)Soochow UniversitySuzhou 215006China Key Laboratory of Advanced Carbon Materials and Wearable Energy Technologies of jiangsu Province Soochow UniversitySuzhou 215006China Key Laboratory of Advanced Optical Manufacturing Technologies of jiangsu Province&Key Laboratory of Modern Optical Technologies of Education Ministry of China Soochow UniversitySuzhou 215006China

Although the efficiency of CH3 NH3 PI3 has been refreshed to 25.2%,stability and toxicity remain the main challenges for its *** search for novel solar-cell absorbers that are highly stable,non-toxic,inexpensive,and highly efficient is now a viable research *** this review,we summarize our recent research into the high-throughput screening and materials design of solar-cell absorbers,including single perovskites,double perovskites,and materials beyond ***3(single perovskite),Ba2 BiNbS6(double perovskite),HgAl2 Se4(spinel),and IrSb3(skutterudite)were discovered to be potential candidates in terms of their high stabilities,appropriate bandgaps,small carrier effective masses,and strong optical absorption.

关键词： solar cell high-throughput materials design first-principles calculations

Substrate screening for superclean graphene growth using firstprinciples calculations

在线全文

学校读者我要写书评

暂无评论

Nano Research 2024年第4期17卷 2216-2222页

作者： Zhihao Li Xiucai Sun Xiaoli Sun wan-jian yin Zhongfan Liu College of Energy Soochow Institute for Energy and Materials InnovationS(SIEMIS)and Jiangsu Provincial Key Laboratory for Advanced Carbon Materials and Wearable Energy TechnologiesSoochow UniversitySuzhou 215006China Graphene Basic Science Research Center Beijing Graphene Institute(BGI)Beijing 100095China Center for Nanochemistry Beijing Science and Engineering Center for NanocarbonsBeijing National Laboratory for Molecular ScienceCollege of Chemistry and Molecular EngineeringPeking UniversityBeijing 100871China

Suppressing the formation of amorphous surface carbon and contaminants during the preparation of graphene by chemical vapor deposition remains an ongoing ***,we analyzed how substrate characteristics affect graphene quality by simulating margin extension,the nucleation process,and defect pegging configurations on mono-crystalline oriented metal substrates with the aim of enhancing graphene *** formation energy and nucleation potential,which are indirect substrate–graphene interaction features,were found to appropriately evaluate graphene *** crystallographic orientation of the metal substrate was discovered to be critical for producing superclean graphene.A low graphene defect density and high nucleation rate on the Cu(100)facet guarantee growth of high-quality graphene,especially in terms of suppressing the formation of amorphous *** addition,rapid kink growth and self-healing on the Cu(100)facet facilitate rapid graphene synthesis,which is also promoted by rapid kink splicing and margin self-repair on this *** study provides theoretical insight useful for the synthesis of superclean graphene.

关键词： superclean graphene defects nucleation metal substrates density functional theory calculations

An approach for full space inverse materials design by combining universal machine learning potential,universal property model,and optimization algorithm

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Science Bulletin 2024年第19期69卷 3066-3074页

作者： Guanjian Cheng Xin-Gao Gong wan-jian yin College of Energy Soochow Institute for Energy and Materials InnovationS(SIEMIS)and Jiangsu Provincial Key Laboratory for Advanced Carbon Materials and Wearable Energy TechnologiesSoochow UniversitySuzhou 215006China Shanghai Qi Zhi Institute Shanghai 200232China Key Laboratory for Computational Physical Sciences(MOE) Institute of Computational Physical SciencesFudan UniversityShanghai 200438China

We present a full space inverse materials design(FSIMD)approach that fully automates the materials design for target physical properties without the need to provide the atomic composition,chemical stoichiometry,and crystal structure in ***,we used density functional theory reference data to train a universal machine learning potential(UPot)and transfer learning to train a universal bulk modulus model(UBmod).Both UPot and UBmod were able to cover materials systems composed of any element among 42 *** with optimization algorithm and enhanced sampling,the FSIMD approach is applied to find the materials with the largest cohesive energy and the largest bulk modulus,***-type ZrC was found to be the material with the largest cohesive *** bulk modulus,diamond was identified to have the largest *** FSIMD approach is also applied to design materials with other multi-objective properties with accuracy limited principally by the amount,reliability,and diversity of the training *** FSIMD approach provides a new way for inverse materials design with other functional properties for practical applications.

关键词： Inverse materials design Universal machine learning potential Graph neural networks Bayesian optimization

Machine learning in materials design:Algorithm and application

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Chinese Physics B 2020年第11期29卷 52-80页

作者： Zhilong Song Xiwen Chen Fanbin Meng Guanjian Cheng Chen wang Zhongti Sun wan-jian yin College of Energy Soochow Institute for Energy and Materials InnovationS(SIEMIS)and Jiangsu Provincial Key Laboratory for Advanced Carbon Materials and Wearable Energy TechnologiesSoochow UniversitySuzhou 215006China

Traditional materials discovery is in ‘trial-and-error’ mode, leading to the issues of low-efficiency, high-cost, and unsustainability in materials design. Meanwhile, numerous experimental and computational trials accumulate enormous quantities of data with multi-dimensionality and complexity, which might bury critical ‘structure–properties’ rules yet unfortunately not well explored. Machine learning(ML), as a burgeoning approach in materials science, may dig out the hidden structure–properties relationship from materials bigdata, therefore, has recently garnered much attention in materials science. In this review, we try to shortly summarize recent research progress in this field, following the ML paradigm:(i) data acquisition →(ii) feature engineering →(iii) algorithm →(iv) ML model →(v) model evaluation →(vi) application. In section of application, we summarize recent work by following the ‘material science tetrahedron’:(i) structure and composition →(ii) property →(iii) synthesis →(iv) characterization, in order to reveal the quantitative structure–property relationship and provide inverse design countermeasures. In addition, the concurrent challenges encompassing data quality and quantity, model interpretability and generalizability, have also been discussed. This review intends to provide a preliminary overview of ML from basic algorithms to applications.

关键词： machine learning materials design structure–property relationship active learning

Invisible vapor catalysis in graphene growth by chemical vapor deposition

在线全文

学校读者我要写书评

暂无评论

Nano Research 2024年第5期17卷 4259-4269页

作者： Xiucai Sun Xiaoting Liu Zhongti Sun Xintong Zhang Yuzhu Wu Yeshu Zhu Yuqing Song Kaicheng Jia Jincan Zhang Luzhao Sun wan-jian yin Zhongfan Liu Center for Nanochemistry Beijing Science and Engineering Center for NanocarbonsBeijing National Laboratory for Molecular ScienceCollege of Chemistry and Molecular EngineeringPeking UniversityBeijing 100871China Beijing Graphene Institute(BGI) Beijing 100095China Academy for Advanced Interdisciplinary Studies Peking UniversityBeijing 100871China School of Materials Science and Engineering Jiangsu UniversityZhenjiang 212013China Department of Engineering University of CambridgeCambridge CB30FAUK College of Energy Soochow Institute for Energy and Materials InnovationS(SIEMIS)Jiangsu Provincial Key Laboratory for Advanced Carbon Materials and Wearable Energy TechnologiesSoochow UniversitySuzhou 215006China

Vapor catalysis was recently found to play a crucial role in superclean graphene growth via chemical vapor decomposition(CVD).However,knowledge of vapor-phase catalysis is scarce,and several fundamental issues,including vapor compositions and their impact on graphene growth,are ***,by combining density functional theory(DFT)calculations,an ideal gas model,and a designed experiment,we found that the vapor was mainly composed of Cui clusters with tens of *** vapor pressure was estimated to be~10^(-12)-10^(-1)1 bar under normal low-pressure CVD system(LPCVD)conditions for graphene growth,and the exposed surface area of Cui clusters in the vapor was 22-269 times that of the Cu substrate surface,highlighting the importance of vapor *** calculations show Cu clusters,represented by Cu17,have strong capabilities for adsorption,dehydrogenation,and decomposition of *** exhibit an adsorption lifetime and reaction flux six orders of magnitude higher than those on the Cu surface,thus providing a sufficient supply of active C atoms for rapid graphene growth and improving the surface cleanliness of the synthesized *** experimental validation showed that increasing the amount of Cu vapor improved the as-synthesized graphene growth rate and surface *** study provides a comprehensive understanding of vapor catalysis and the fundamental basis of vapor control for superclean graphene rapid growth.

关键词： vapor catalysis graphene growth chemical vapor deposition first-principles calculation

Bayesian optimization based on a unified figure of merit for accelerated materials screening:A case study of halide perovskites

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Science China Materials 2020年第6期63卷 1024-1035页

作者： Xiwen Chen Chen wang Zhenzhu Li Zhufeng Hou wan-jian yin College of Energy Soochow Institute for Energy and Materials Innovations(SIEMIS)and Jiangsu Provincial Key Laboratory for Advanced Carbon Materials and Wearable Energy TechnologiesSoochow UniversitySuzhou 215006China State Key Laboratory of Structural Chemistry Fujian Institute of Research on the Structure of MatterChinese Academy of SciencesFuzhou 350002China Key Lab of Advanced Optical Manufacturing Technologies of jiangsu Province&Key Lab of Modern Optical Technologies of Education Ministry of China Soochow UniversitySuzhou 215006China

The figure of merit is of crucial importance in materials design to search for candidates with optimal *** the field of photovoltaics,the bandgap(E_g)is a well-recognized figure of merit for screening solar cell absorbers subject to the Shockley-Queisser *** this paper,the bandgap as the figure of merit is challenged since an ideal solar cell absorber requires multiple criteria such as stability,optical absorption,and carrier *** criteria make the quantitative description of material candidates difficult and computationally *** halide perovskites as an example,we combine thermodynamic stability(ΔHd)and Eginto a unified figure of merit and use Bayesian optimization(BO)to accelerate materials *** have found that,in comparison to an exhaustive search via multiple parameters,BO based on the unified figure of merit can screen optimal candidates(E_g,PBEbetween 0.6–1.2 eV,ΔHd>-29 meV per atom)more ***,the proposed method opens a viable route for the search of optimal solar cell absorbers from a large amount of material candidates with less computational cost.

关键词： descriptor machine learning Bayesian optimization DFT calculations