Inverted perovskite solar cells have gained prominence in industrial advancement due to their easy fabrication,low hysteresis effects,and high stability.Despite these advantages,their efficiency is currently limited b...Inverted perovskite solar cells have gained prominence in industrial advancement due to their easy fabrication,low hysteresis effects,and high stability.Despite these advantages,their efficiency is currently limited by excessive defects and poor carrier transport at the perovskite-electrode interface,particularly at the buried interface between the perovskite and transparent conductive oxide(TCO).Recent efforts in the perovskite community have focused on designing novel self-assembled molecules(SAMs)to improve the quality of the buried interface.However,a notable gap remains in understanding the regulation of atomic-scale interfacial properties of SAMs between the perovskite and TCO interfaces.This understanding is crucial,particularly in terms of identifying chemically active anchoring groups.In this study,we used the star SAM([2-(9H-carbazol-9-yl)ethyl]phosphonic acid)as the base structure to investigate the defect passivation effects of eight common anchoring groups at the perovskite-TCO interface.Our findings indicate that the phosphonic and boric acid groups exhibit notable advantages.These groups fulfill three key criteria:they provide the greatest potential for defect passivation,exhibit stable adsorption with defects,and exert significant regulatory effects on interface dipoles.Ionized anchoring groups exhibit enhanced passivation capabilities for defect energy levels due to their superior Lewis base properties,which effectively neutralize local charges near defects.Among various defect types,iodine vacancies are the easiest to passivate,whereas iodine-substituted lead defects are the most challenging to passivate.Our study provides comprehensive theoretical insights and inspiration for the design of anchoring groups in SAMs,contributing to the ongoing development of more efficient inverted perovskite solar cells.展开更多
Site disorder exists in some practical semiconductors and can significantly impact their intrinsic properties both beneficially and detrimentally.However,the uncertain local order and structure pose a challenge for ex...Site disorder exists in some practical semiconductors and can significantly impact their intrinsic properties both beneficially and detrimentally.However,the uncertain local order and structure pose a challenge for experimental and theoretical research.Especially,it hinders the investigation of the effects of the diverse local atomic environments resulting from the site disorder.We employ the special quasi-random structure method to perform first-principles research on connection between local site disorder and electronic/optical properties,using cationdisordered AgBiS_(2)(rock salt phase)as an example.We predict that cation-disordered AgBiS_(2)has a bandgap ranging from 0.6 to 0.8 eV without spin-orbit coupling and that spin-orbit coupling reduces this by approximately 0.3 eV.We observe the effects of local structural features in the disordered lattice,such as the one-dimensional chain-like aggregation of cations that results in formation of doping energy bands near the band edges,formation and broadening of band-tail states,and the disturbance in the local electrostatic potential,which significantly reduces the bandgap and stability.The influence of these ordered features on the optical properties is confined to alterations in the bandgap and does not markedly affect the joint density of states or optical absorption.Our study provides a research roadmap for exploring the electronic structure of site-disordered semiconductor materials,suggests that the ordered chain-like aggregation of cations is an effective way to regulate the bandgap of AgBiS_(2),and provides insight into how variations in local order associated with processing can affect properties.展开更多
Open framework structures(e.g.,ScF_(3),Sc_(2)W_(3O)_(12),etc.)exhibit significant potential for thermal expansion tailoring owing to their high atomic vibrational degrees of freedom and diverse connectivity between po...Open framework structures(e.g.,ScF_(3),Sc_(2)W_(3O)_(12),etc.)exhibit significant potential for thermal expansion tailoring owing to their high atomic vibrational degrees of freedom and diverse connectivity between polyhedral units,displaying positive/negative thermal expansion(PTE/NTE)coefficients at a certain temperature.Despite the proposal of several physical mechanisms to explain the origin of NTE,an accurate mapping relationship between the structural–compositional properties and thermal expansion behavior is still lacking.This deficiency impedes the rapid evaluation of thermal expansion properties and hinders the design and development of such materials.We developed an algorithm for identifying and characterizing the connection patterns of structural units in open-framework structures and constructed a descriptor set for the thermal expansion properties of this system,which is composed of connectivity and elemental information.Our developed descriptor,aided by machine learning(ML)algorithms,can effectively learn the thermal expansion behavior in small sample datasets collected from literature-reported experimental data(246 samples).The trained model can accurately distinguish the thermal expansion behavior(PTE/NTE),achieving an accuracy of 92%.Additionally,our model predicted six new thermodynamically stable NTE materials,which were validated through first-principles calculations.Our results demonstrate that developing effective descriptors closely related to thermal expansion properties enables ML models to make accurate predictions even on small sample datasets,providing a new perspective for understanding the relationship between connectivity and thermal expansion properties in the open framework structure.The datasets that were used to support these results are available on Science Data Bank,accessible via the link https://doi.org/10.57760/sciencedb.j00113.00100.展开更多
With the rapid development of artificial intelligence and machine learning(ML)methods,materials science is rapidly entering the era of data-driven materials informatics.ML models serve as the most crucial component,cl...With the rapid development of artificial intelligence and machine learning(ML)methods,materials science is rapidly entering the era of data-driven materials informatics.ML models serve as the most crucial component,closely bridging material structure and material properties.There is a considerable difference in the prediction performance of different ML methods for material systems.Herein,we evaluated three categories(linear,kernel,and nonlinear methods)of models,with twelve ML algorithms commonly used in the materials field.In addition,halide perovskite was chosen as an example to evaluate the fitting performance of different models.We constructed a total dataset of 540 halide perovskites and 72 features,with formation energy and bandgap as target properties.We found that different categories of ML models show similar trends for different target properties.Among them,the difference between the models is enormous for the formation energy,with the coefficient of determination(R2)range 0.69-0.953.The fitting performance between the models is closer for bandgap,with the R^(2)range 0.941-0.997.The nonlinear-ensemble model shows the best fitting performance for both the formation energy and the bandgap.It shows that the nonlinear-ensemble model,constructed by combining multiple weak learners,effectively describes the nonlinear relationship between material features and target property.In addition,the extreme gradient boosting decision tree model shows the most superior results among all the models and searches for two new descriptors that are crucial for formation energy and bandgap.Our work provides useful guidance for the selection of effective machine learning methods in the data-mining studies of specific material systems.展开更多
GaTe is a two-dimensional Ⅲ-Ⅵ semiconductor with suitable direct bandgap of~1.65 eV and high photoresponsivity,which makes it a promising candidate for optoelectronic applications.GaTe exists in two crystalline phas...GaTe is a two-dimensional Ⅲ-Ⅵ semiconductor with suitable direct bandgap of~1.65 eV and high photoresponsivity,which makes it a promising candidate for optoelectronic applications.GaTe exists in two crystalline phases:monoclinic(m-GaTe,with space group C2/m) and hexagonal(h-GaTe,with space group P63/mmc).The phase transition between the two phases was reported under temperature-varying conditions,such as annealing,laser irradiation,etc.The explicit phase transition temperature and energy barrier during the temperature-induced phase transition have not been explored.In this work,we present a comprehensive study of the phase transition process by using first-principles energetic and phonon calculations within the quasi-harmonic approximation framework.We predicted that the phase transition from h-GaTe to m-GaTe occurs at the temperature decreasing to 261 K.This is in qualitative agreement with the experimental observations.It is a two-step transition process with energy barriers 199 meV and 288 meV,respectively.The relatively high energy barriers demonstrate the irreversible nature of the phase transition.The electronic and phonon properties of the two phases were further investigated by comparison with available experimental and theoretical results.Our results provide insightful understanding on the process of temperature-induced phase transition of GaTe.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
基金supported by the National Natural Science Foundation of China(Grant Nos.62321166653,22090044,and 12350410372).Calculations were performed in part at the high-performance computing center of Jilin University.
文摘Inverted perovskite solar cells have gained prominence in industrial advancement due to their easy fabrication,low hysteresis effects,and high stability.Despite these advantages,their efficiency is currently limited by excessive defects and poor carrier transport at the perovskite-electrode interface,particularly at the buried interface between the perovskite and transparent conductive oxide(TCO).Recent efforts in the perovskite community have focused on designing novel self-assembled molecules(SAMs)to improve the quality of the buried interface.However,a notable gap remains in understanding the regulation of atomic-scale interfacial properties of SAMs between the perovskite and TCO interfaces.This understanding is crucial,particularly in terms of identifying chemically active anchoring groups.In this study,we used the star SAM([2-(9H-carbazol-9-yl)ethyl]phosphonic acid)as the base structure to investigate the defect passivation effects of eight common anchoring groups at the perovskite-TCO interface.Our findings indicate that the phosphonic and boric acid groups exhibit notable advantages.These groups fulfill three key criteria:they provide the greatest potential for defect passivation,exhibit stable adsorption with defects,and exert significant regulatory effects on interface dipoles.Ionized anchoring groups exhibit enhanced passivation capabilities for defect energy levels due to their superior Lewis base properties,which effectively neutralize local charges near defects.Among various defect types,iodine vacancies are the easiest to passivate,whereas iodine-substituted lead defects are the most challenging to passivate.Our study provides comprehensive theoretical insights and inspiration for the design of anchoring groups in SAMs,contributing to the ongoing development of more efficient inverted perovskite solar cells.
基金supported by the National Natural Science Foundation of China(Grant Nos.62125402,22090044,and 12350410372)the National Key Research and Development Program of China(Grant No.2022YFA1402501)Graduate Innovation Fund of Jilin University(Grant No.2022118)。
文摘Site disorder exists in some practical semiconductors and can significantly impact their intrinsic properties both beneficially and detrimentally.However,the uncertain local order and structure pose a challenge for experimental and theoretical research.Especially,it hinders the investigation of the effects of the diverse local atomic environments resulting from the site disorder.We employ the special quasi-random structure method to perform first-principles research on connection between local site disorder and electronic/optical properties,using cationdisordered AgBiS_(2)(rock salt phase)as an example.We predict that cation-disordered AgBiS_(2)has a bandgap ranging from 0.6 to 0.8 eV without spin-orbit coupling and that spin-orbit coupling reduces this by approximately 0.3 eV.We observe the effects of local structural features in the disordered lattice,such as the one-dimensional chain-like aggregation of cations that results in formation of doping energy bands near the band edges,formation and broadening of band-tail states,and the disturbance in the local electrostatic potential,which significantly reduces the bandgap and stability.The influence of these ordered features on the optical properties is confined to alterations in the bandgap and does not markedly affect the joint density of states or optical absorption.Our study provides a research roadmap for exploring the electronic structure of site-disordered semiconductor materials,suggests that the ordered chain-like aggregation of cations is an effective way to regulate the bandgap of AgBiS_(2),and provides insight into how variations in local order associated with processing can affect properties.
基金the National Natural Science Foundation of China(Grant Nos.12004131,22090044,62125402,and 92061113)。
文摘Open framework structures(e.g.,ScF_(3),Sc_(2)W_(3O)_(12),etc.)exhibit significant potential for thermal expansion tailoring owing to their high atomic vibrational degrees of freedom and diverse connectivity between polyhedral units,displaying positive/negative thermal expansion(PTE/NTE)coefficients at a certain temperature.Despite the proposal of several physical mechanisms to explain the origin of NTE,an accurate mapping relationship between the structural–compositional properties and thermal expansion behavior is still lacking.This deficiency impedes the rapid evaluation of thermal expansion properties and hinders the design and development of such materials.We developed an algorithm for identifying and characterizing the connection patterns of structural units in open-framework structures and constructed a descriptor set for the thermal expansion properties of this system,which is composed of connectivity and elemental information.Our developed descriptor,aided by machine learning(ML)algorithms,can effectively learn the thermal expansion behavior in small sample datasets collected from literature-reported experimental data(246 samples).The trained model can accurately distinguish the thermal expansion behavior(PTE/NTE),achieving an accuracy of 92%.Additionally,our model predicted six new thermodynamically stable NTE materials,which were validated through first-principles calculations.Our results demonstrate that developing effective descriptors closely related to thermal expansion properties enables ML models to make accurate predictions even on small sample datasets,providing a new perspective for understanding the relationship between connectivity and thermal expansion properties in the open framework structure.The datasets that were used to support these results are available on Science Data Bank,accessible via the link https://doi.org/10.57760/sciencedb.j00113.00100.
基金supported by the National Natural Science Foundation of China(Grants Nos.62125402 and 92061113)。
文摘With the rapid development of artificial intelligence and machine learning(ML)methods,materials science is rapidly entering the era of data-driven materials informatics.ML models serve as the most crucial component,closely bridging material structure and material properties.There is a considerable difference in the prediction performance of different ML methods for material systems.Herein,we evaluated three categories(linear,kernel,and nonlinear methods)of models,with twelve ML algorithms commonly used in the materials field.In addition,halide perovskite was chosen as an example to evaluate the fitting performance of different models.We constructed a total dataset of 540 halide perovskites and 72 features,with formation energy and bandgap as target properties.We found that different categories of ML models show similar trends for different target properties.Among them,the difference between the models is enormous for the formation energy,with the coefficient of determination(R2)range 0.69-0.953.The fitting performance between the models is closer for bandgap,with the R^(2)range 0.941-0.997.The nonlinear-ensemble model shows the best fitting performance for both the formation energy and the bandgap.It shows that the nonlinear-ensemble model,constructed by combining multiple weak learners,effectively describes the nonlinear relationship between material features and target property.In addition,the extreme gradient boosting decision tree model shows the most superior results among all the models and searches for two new descriptors that are crucial for formation energy and bandgap.Our work provides useful guidance for the selection of effective machine learning methods in the data-mining studies of specific material systems.
基金Project supported by the National Natural Science Foundation of China(Grant No.62004080)Postdoctoral Innovative Talents Supporting Program(Grant No.BX20190143)+1 种基金China Postdoctoral Science Foundation(2020M670834)Jilin Province Science and Technology Development Program,China(Grant No.20190201016JC)。
文摘GaTe is a two-dimensional Ⅲ-Ⅵ semiconductor with suitable direct bandgap of~1.65 eV and high photoresponsivity,which makes it a promising candidate for optoelectronic applications.GaTe exists in two crystalline phases:monoclinic(m-GaTe,with space group C2/m) and hexagonal(h-GaTe,with space group P63/mmc).The phase transition between the two phases was reported under temperature-varying conditions,such as annealing,laser irradiation,etc.The explicit phase transition temperature and energy barrier during the temperature-induced phase transition have not been explored.In this work,we present a comprehensive study of the phase transition process by using first-principles energetic and phonon calculations within the quasi-harmonic approximation framework.We predicted that the phase transition from h-GaTe to m-GaTe occurs at the temperature decreasing to 261 K.This is in qualitative agreement with the experimental observations.It is a two-step transition process with energy barriers 199 meV and 288 meV,respectively.The relatively high energy barriers demonstrate the irreversible nature of the phase transition.The electronic and phonon properties of the two phases were further investigated by comparison with available experimental and theoretical results.Our results provide insightful understanding on the process of temperature-induced phase transition of GaTe.
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.
