High-pressure research has emerged as a pivotal approach for advancing our understanding and development of optoelectronic materials,which are vital for a wide range of applications,including photovoltaics,light-emitt...High-pressure research has emerged as a pivotal approach for advancing our understanding and development of optoelectronic materials,which are vital for a wide range of applications,including photovoltaics,light-emitting devices,and photodetectors.This review highlights various in situ characterization methods employed in high-pressure research to investigate the optical,electronic,and structural properties of optoelectronic materials.We explore the advances that have been made in techniques such as X-ray diffraction,absorption spectroscopy,nonlinear optics,photoluminescence spectroscopy,Raman spectroscopy,and photoresponse measurement,emphasizing how these methods have enhanced the elucidation of structural transitions,bandgap modulation,performance optimization,and carrier dynamics engineering.These insights underscore the pivotal role of high-pressure techniques in optimizing and tailoring optoelectronic materials for future applications.展开更多
A comprehensive understanding of the intrinsic piezoelectric anisotropy stemming from diverse chemical and physical factors is a key step for the rational design of highly anisotropic materials.We performed high-throu...A comprehensive understanding of the intrinsic piezoelectric anisotropy stemming from diverse chemical and physical factors is a key step for the rational design of highly anisotropic materials.We performed high-throughput calculations on tetragonal ABO3 perovskites to investigate the overall characteristics of their piezoelectricity and the interplay between lattice,displacement,polarization,and elasticity.Among the screened 123 types of perovskites,the structural tetragonality is naturally divided into two categories:normal tetragonal(c/a ratio<1.1)and super-tetragonal(c/a ratio>1.17),exhibiting distinct chemical features,ferroelectric,elastic,and piezoelectric properties.Charge analysis revealed the mechanisms underlying polarization saturation and piezoelectricity suppression in the super-tetragonal region,which also produces an inherent contradiction between high piezoelectric coefficient d33 and large piezoelectric anisotropy ratio|d33/d31|.Both the polarization axis and elastic softness direction are strongly correlated to piezoelectric anisotropy,which jointly determines the direction of maximum longitudinal piezoelectric response d_(33).The validity and deficiencies of the widely utilized|d_(33)/d_(31)|ratio for representing piezoelectric anisotropy were reevaluated.展开更多
The semi-empirical pseudopotential method(SEPM)has been widely applied to provide computational insights into the electronic structure,photophysics,and charge carrier dynamics of nanoscale materials.We present“DeepPs...The semi-empirical pseudopotential method(SEPM)has been widely applied to provide computational insights into the electronic structure,photophysics,and charge carrier dynamics of nanoscale materials.We present“DeepPseudopot”,a machine-learned atomistic pseudopotential model that extends the SEPM framework by combining a flexible neural network representation of the local pseudopotential with parameterized non-local and spin-orbit coupling terms.Trained on bulk quasiparticle band structures and deformation potentials from GW calculations,the model captures many-body and relativistic effects with very high accuracy across diverse semiconducting materials,as illustrated for silicon and group III-V semiconductors.DeepPseudopot’s accuracy,efficiency,and transferability make it well-suited for data-driven in silico design and discovery of novel optoelectronic nanomaterials.展开更多
The rapid advancement of nanomaterials and their integration into biosensing and energy storage applications have revolutionized both biomedical diagnostics and sustainable energy solutions.In this special issue of Ad...The rapid advancement of nanomaterials and their integration into biosensing and energy storage applications have revolutionized both biomedical diagnostics and sustainable energy solutions.In this special issue of Advanced Sensor and Energy Materials,we bring together cutting-edge research and comprehensive reviews that highlight the latest de-velopments in these dynamic fields.展开更多
The rapid rise of generative artificial intelligence is reshaping materials discovery by offering new ways to propose crystal structures and,in some cases,even predict desired properties.This review provides a compreh...The rapid rise of generative artificial intelligence is reshaping materials discovery by offering new ways to propose crystal structures and,in some cases,even predict desired properties.This review provides a comprehensive survey of recent advancements in generative models specifically for inorganic crystalline materials.We outline architectures,representations,conditioning mechanisms,data sources,metrics,and applications,and organize existing models into a unified taxonomy.展开更多
Global optimization of crystal compositions is a significant yet computationally intensive method to identify stable structures within chemical space.The specific physical properties linked to a threedimensional atomi...Global optimization of crystal compositions is a significant yet computationally intensive method to identify stable structures within chemical space.The specific physical properties linked to a threedimensional atomic arrangement make this an essential task in the development of new materials.We present a method that efficiently uses active learning of neural network force fields for structure relaxation,minimizing the required number of steps in the process.This is achieved by neural network force fields equipped with uncertainty estimation,which iteratively guide a pool of randomly generated candidates toward their respective local minima.Using this approach,we are able to effectively identify themost promising candidates for further evaluation using density functional theory(DFT).Our method not only reliably reduces computational costs by up to two orders of magnitude across the benchmark systemsSi_(16),Na_(8)Cl_(8),Ga_(8)As_(8)and Al_(4)O_(6)but also excels in finding themost stable minimum for the unseen,more complex systems Si46 and Al16O24.Moreover,we demonstrate at the example of Si_(16)that our method can find multiple relevant local minima while only adding minor computational effort.展开更多
基金supported by the National Nature Science Foundation of China(NSFC)(Grant Nos.22275004,62274040,and 62304046)the Shanghai Science and Technology Committee(Grant No.22JC1410300)+2 种基金the Shanghai Key Laboratory of Novel Extreme Condition Materials(Grant No.22dz2260800)the National Key Research and Development Program of China(Grant No.2022YFE0137400)the Shanghai Science and Technology Innovationaction Plan(Grant No.24DZ3001200).
文摘High-pressure research has emerged as a pivotal approach for advancing our understanding and development of optoelectronic materials,which are vital for a wide range of applications,including photovoltaics,light-emitting devices,and photodetectors.This review highlights various in situ characterization methods employed in high-pressure research to investigate the optical,electronic,and structural properties of optoelectronic materials.We explore the advances that have been made in techniques such as X-ray diffraction,absorption spectroscopy,nonlinear optics,photoluminescence spectroscopy,Raman spectroscopy,and photoresponse measurement,emphasizing how these methods have enhanced the elucidation of structural transitions,bandgap modulation,performance optimization,and carrier dynamics engineering.These insights underscore the pivotal role of high-pressure techniques in optimizing and tailoring optoelectronic materials for future applications.
基金supported by National Natural Science Foundation of China(No.52472133,12347115,11929401,52271007,12274278,12074241,and 52130204)the Original exploration project of Shanghai Natural Science Foundation(No.22ZR1481100)+4 种基金The open fund of National Key Laboratory of Science and Technology on Underwater Acoustic Antagonizing(Grant No.JCKY2024207CH12)Science and Technology Commission of Shanghai Municipality(No.22XD1400900,20501130600,21JC1402600,and 21JC1402700)the Major Science and Technology Projects of Shanxi Province(No.202201150501024)China Postdoctoral Science Foundation(No.2024M760690)High-Performance Computing Center,Shanghai Technical Service Center of Science and Engineering Computing,Shanghai University.Work at HDU was supported by Zhejiang Provincial Natural Science Foundation(QN25A040026).Dr.Jia also appreciated the discussions with P.Z.
文摘A comprehensive understanding of the intrinsic piezoelectric anisotropy stemming from diverse chemical and physical factors is a key step for the rational design of highly anisotropic materials.We performed high-throughput calculations on tetragonal ABO3 perovskites to investigate the overall characteristics of their piezoelectricity and the interplay between lattice,displacement,polarization,and elasticity.Among the screened 123 types of perovskites,the structural tetragonality is naturally divided into two categories:normal tetragonal(c/a ratio<1.1)and super-tetragonal(c/a ratio>1.17),exhibiting distinct chemical features,ferroelectric,elastic,and piezoelectric properties.Charge analysis revealed the mechanisms underlying polarization saturation and piezoelectricity suppression in the super-tetragonal region,which also produces an inherent contradiction between high piezoelectric coefficient d33 and large piezoelectric anisotropy ratio|d33/d31|.Both the polarization axis and elastic softness direction are strongly correlated to piezoelectric anisotropy,which jointly determines the direction of maximum longitudinal piezoelectric response d_(33).The validity and deficiencies of the widely utilized|d_(33)/d_(31)|ratio for representing piezoelectric anisotropy were reevaluated.
基金supported by the National Science Foundation Division of Chemistry, under the Chemical Theory, Models and Computational Methods (CTMC) program, grant number CHE-2449564Methods used to describe the vibronic properties of NCs were provided by the center on “Traversing the death valley separating short and long times in non-equilibrium quantum dynamical simulations of real materials”, which is funded by the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research and Office of Basic Energy Sciences, Scientific Discovery through Advanced Computing (SciDAC) program, under Award No. DE-SC0022088+2 种基金Measured optical properties of III-V NCs were supported by the National Science Foundation Science and Technology Center (STC) for Integration of Modern Optoelectronic Materials on Demand (IMOD) under Cooperative Agreement No. DMR-2019444This research used resources of the National Energy Research Scientific Computing Center (NERSC)a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231 using NERSC award BES-ERCAP0032503.
文摘The semi-empirical pseudopotential method(SEPM)has been widely applied to provide computational insights into the electronic structure,photophysics,and charge carrier dynamics of nanoscale materials.We present“DeepPseudopot”,a machine-learned atomistic pseudopotential model that extends the SEPM framework by combining a flexible neural network representation of the local pseudopotential with parameterized non-local and spin-orbit coupling terms.Trained on bulk quasiparticle band structures and deformation potentials from GW calculations,the model captures many-body and relativistic effects with very high accuracy across diverse semiconducting materials,as illustrated for silicon and group III-V semiconductors.DeepPseudopot’s accuracy,efficiency,and transferability make it well-suited for data-driven in silico design and discovery of novel optoelectronic nanomaterials.
文摘The rapid advancement of nanomaterials and their integration into biosensing and energy storage applications have revolutionized both biomedical diagnostics and sustainable energy solutions.In this special issue of Advanced Sensor and Energy Materials,we bring together cutting-edge research and comprehensive reviews that highlight the latest de-velopments in these dynamic fields.
文摘The rapid rise of generative artificial intelligence is reshaping materials discovery by offering new ways to propose crystal structures and,in some cases,even predict desired properties.This review provides a comprehensive survey of recent advancements in generative models specifically for inorganic crystalline materials.We outline architectures,representations,conditioning mechanisms,data sources,metrics,and applications,and organize existing models into a unified taxonomy.
基金N.W.A.G.and M.G.contributed to this research while working at the BASLEARN-TU Berlin/BASF Joint Lab for Machine Learning,co-financed by TU Berlin and BASF SE.K.T.S.contributed to this research while working at TU Berlin and BIFOLD with grant number 01IS18037Asupported by JSPS KAKENHI Grant Number JP23H05457 and by JST-CREST Grant Number JPMJCR22O2.We thank Jonas Lederer and Klaus-Robert Müller for insightful discussions and feedback.
文摘Global optimization of crystal compositions is a significant yet computationally intensive method to identify stable structures within chemical space.The specific physical properties linked to a threedimensional atomic arrangement make this an essential task in the development of new materials.We present a method that efficiently uses active learning of neural network force fields for structure relaxation,minimizing the required number of steps in the process.This is achieved by neural network force fields equipped with uncertainty estimation,which iteratively guide a pool of randomly generated candidates toward their respective local minima.Using this approach,we are able to effectively identify themost promising candidates for further evaluation using density functional theory(DFT).Our method not only reliably reduces computational costs by up to two orders of magnitude across the benchmark systemsSi_(16),Na_(8)Cl_(8),Ga_(8)As_(8)and Al_(4)O_(6)but also excels in finding themost stable minimum for the unseen,more complex systems Si46 and Al16O24.Moreover,we demonstrate at the example of Si_(16)that our method can find multiple relevant local minima while only adding minor computational effort.
