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
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.
