In this work,a multi-stage material design framework combining machine learning techniques with density functional theory is established to reveal the mechanism of phase stabilization in HfO_(2)based ferroelectric mat...In this work,a multi-stage material design framework combining machine learning techniques with density functional theory is established to reveal the mechanism of phase stabilization in HfO_(2)based ferroelectric materials.The ferroelectric phase fractions based on a more stringent relationship of phase energy differences is proposed as an evaluation criterion for the ferroelectric performance of hafnium-based materials.Based on the Boltzmann distribution theory,the abstract phase energy difference is converted into an intuitive phase fraction distribution mapping.A large-scale prediction of unknown dopants is conducted within the material design framework,and gallium(Ga)is identified as a new dopant for HfO_(2).Both experiments and density functional theory calculations demonstrate that Ga is an excellent dopant for ferroelectric hafnium oxide,especially,the experimentally determined variation trends of ferroelectric phase fraction and polarization properties with Ga doping concentration are in good agreement with the predictions given by machine learning.This work provides a new perspective from machine learning to deepen the understanding of the ferroelectric properties of HfO_(2)materials,offering fresh insights into the design and performance prediction of HfO_(2)ferroelectric thin films.展开更多
Two-dimensional(2D)materials demonstrate exceptional sliding ferroelectricity,owing to their facilitated interface charge transfer and controllable interlayer sliding.The development of highperformance sliding ferroel...Two-dimensional(2D)materials demonstrate exceptional sliding ferroelectricity,owing to their facilitated interface charge transfer and controllable interlayer sliding.The development of highperformance sliding ferroelectric materials necessitates substantial sliding-induced polarization alongside minimal energy barriers for fatigue resistance.However,since both the sliding-induced ferroelectric out-of-plane polarization(OOP)and energy barriers are governed by interfacial charge transfer,these two critical parameters exhibit intrinsic coupling characteristics.The absence of the underlying mechanism,compounded by the lack of sliding ferroelectricity descriptor,fundamentally impedes the rational design of high-performance sliding ferroelectrics.In this work,we find the interfacial differential charge(IDC)transfer is an intrinsic parameter to link the sliding ferroelectricity and sliding energy barrier.Tracking all of the reported sliding ferroelectric materials,the slidinginducedOOPis found to be proportional to the dipole moments of asymmetric IDC distributions,while the sliding energy barrier is proportional to the absolute difference of IDC transfer.Leveraging highthroughput screening,45 sliding ferroelectric candidates over 2000 homobilayer junctions are identified with superior sliding ferroelectric performance than MoS_(2).Then,a sliding ferroelectricity descriptor is proposed,that is OOP to the ratio between sliding energy barrier and cohesion energy.We further show that moirésuperlattices can suppress net IDC transfer,enabling almost zero sliding energy barrier,but OOP switching during sliding.These insights elucidate the atomic origins of sliding ferroelectricity and establish a predictive framework for designing energy-efficient,fatigue-resistant ferroelectric devices.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.92164108,U23A20322,12072307,62027818,11974320,61804130)the Provincial Natural Science Foundation of Hunan(Grant Nos.2023JJ30599,2023JJ50009)the National Key Research and Development Program of China(2023YFF0719600,2021YFB4000800).
文摘In this work,a multi-stage material design framework combining machine learning techniques with density functional theory is established to reveal the mechanism of phase stabilization in HfO_(2)based ferroelectric materials.The ferroelectric phase fractions based on a more stringent relationship of phase energy differences is proposed as an evaluation criterion for the ferroelectric performance of hafnium-based materials.Based on the Boltzmann distribution theory,the abstract phase energy difference is converted into an intuitive phase fraction distribution mapping.A large-scale prediction of unknown dopants is conducted within the material design framework,and gallium(Ga)is identified as a new dopant for HfO_(2).Both experiments and density functional theory calculations demonstrate that Ga is an excellent dopant for ferroelectric hafnium oxide,especially,the experimentally determined variation trends of ferroelectric phase fraction and polarization properties with Ga doping concentration are in good agreement with the predictions given by machine learning.This work provides a new perspective from machine learning to deepen the understanding of the ferroelectric properties of HfO_(2)materials,offering fresh insights into the design and performance prediction of HfO_(2)ferroelectric thin films.
基金support of National Key Research and Development Program(2024YFA1209801)National Natural Science Foundation of China(12325204,12472108)+2 种基金Fundamental Research Funds for the Central Universities(xxj032025042)the Young Talent Program of Shaanxi Association of Science and Technology(20230509)Scientific Research Program of Shaanxi province(2023JC-XJ-02).
文摘Two-dimensional(2D)materials demonstrate exceptional sliding ferroelectricity,owing to their facilitated interface charge transfer and controllable interlayer sliding.The development of highperformance sliding ferroelectric materials necessitates substantial sliding-induced polarization alongside minimal energy barriers for fatigue resistance.However,since both the sliding-induced ferroelectric out-of-plane polarization(OOP)and energy barriers are governed by interfacial charge transfer,these two critical parameters exhibit intrinsic coupling characteristics.The absence of the underlying mechanism,compounded by the lack of sliding ferroelectricity descriptor,fundamentally impedes the rational design of high-performance sliding ferroelectrics.In this work,we find the interfacial differential charge(IDC)transfer is an intrinsic parameter to link the sliding ferroelectricity and sliding energy barrier.Tracking all of the reported sliding ferroelectric materials,the slidinginducedOOPis found to be proportional to the dipole moments of asymmetric IDC distributions,while the sliding energy barrier is proportional to the absolute difference of IDC transfer.Leveraging highthroughput screening,45 sliding ferroelectric candidates over 2000 homobilayer junctions are identified with superior sliding ferroelectric performance than MoS_(2).Then,a sliding ferroelectricity descriptor is proposed,that is OOP to the ratio between sliding energy barrier and cohesion energy.We further show that moirésuperlattices can suppress net IDC transfer,enabling almost zero sliding energy barrier,but OOP switching during sliding.These insights elucidate the atomic origins of sliding ferroelectricity and establish a predictive framework for designing energy-efficient,fatigue-resistant ferroelectric devices.
