Na_(3)MX_(4) (M=P, Sb and X=S, Se) and its doped analogues are considered as a promising material in room-temperature (RT) Na^(+)-conducting solid electrolytes. Herein, we first report that stoichiometric Na_(3)(W_(x)...Na_(3)MX_(4) (M=P, Sb and X=S, Se) and its doped analogues are considered as a promising material in room-temperature (RT) Na^(+)-conducting solid electrolytes. Herein, we first report that stoichiometric Na_(3)(W_(x)Si_(x)Sb_(1-2)_(x))S_(4) with no nominal vacancies shows significantly high ionic conductivity at RT (σRT) when compared with Na_(3)SbS_(4). The σRT increases continuously with increases in ‘x’, revealing the highest σRT of 13.2 mS cm^(-1) and the lowest activation of 0.16 eV in cubic Na_(3)(W_(0.2)Si_(0.2)Sb_(0.6))S_(4). Further increases in ‘x’ result in the formation of a glassy phase and a reduction in σRT. The σRT of Na_(3)(W_(0.2)Si_(0.2)Sb_(0.6))S_(4) is the highest in stoichiometric Na_(3)MX_(4) known to date and suggests that the Na^(+) diffusion is influenced by the dopant types as well as structural defects. Ab initio molecular dynamics also reveal the improvement of σRT with increases in ‘x’, but the presence of naturally formed vacancies that are commonly observed in Na_(3)MX_(4). The electronic conductivity of Na_(3)(W_(0.2)Si_(0.2)Sb_(0.6))S_(4) is also low (ca. 10^(-6) mS cm^(-1)). However, the cathodic stability is insufficient when W^(6+) and/or Si^(4+) are doped. Therefore, a solid-state cell (Na_(15)Sn_(4) ∥ TiS_(2)) is fabricated with an interlayer of Na_(3)SbS_(4) between Na_(15)Sn_(4) and Na_(3)(W_(0.2)Si_(0.2)Sb_(0.6))S_(4), and its excellent compatibility with a cathode is demonstrated.展开更多
Cathode materials in potassium ion batteries(KIBs)generally exhibit low charge storage capabilities when compared with cathode materials implemented in lithium or sodium ion batteries.In this work,K_(0.78)Fe_(1.60)S_(...Cathode materials in potassium ion batteries(KIBs)generally exhibit low charge storage capabilities when compared with cathode materials implemented in lithium or sodium ion batteries.In this work,K_(0.78)Fe_(1.60)S_(2)is described as a high capacity KIB cathode that exhibits mixed anion/cation redox behaviors during charge/discharge(C/D).When charged to 3.2 V vs.K/K^(+),K^(+)extraction occurs along with simultaneous oxidations of S^(2−)to S_(2)^(2−)and Fe(II)to Fe(III).During subsequent discharge to 1.5 V,this process is reversed,in addition to a further reduction of Fe(II)to Fe(I).After a few C/D cycles,K_(0.78)Fe_(1.6)0S_(2)reversibly delivers 0.69 K^(+)with a capacity of 100.5 mA h g^(−1)(i.e.,K_(0.20)Fe_(1.6)0S_(2)⇆K_(0.89)Fe_(1.6)0S_(2)).The evolution of S_(2)−and Fe(II)valence states along with a lack of discernable changes in crystallographic dimensions clearly confirms the concomitant redox of anions and cations with C/D.Density functional theory calculations also validate the possibility of mixed redox reactions in K_(0.78)Fe_(1.6)0S_(2).Unique structural features of K_(0.78)Fe_(1.60)S_(2)(layers consisting of edge-shared FeS_(4)tetrahedra with partial Fe vacancies)result in high K^(+)diffusion coefficients that are unprecedented(ca.10^(−9)cm^(2)s^(−1)),which contributes to an excellent rate capability(56.3 mA h g^(−1)at 1000 mA^(g−1)vs.100.5 mA h g^(−1)at 20 mA g^(−1)).Nudged elastic band calculations also reveal that the diffusion preferentially occurs along[100]directions with a low activation energy barrier of 0.41 eV.展开更多
Multi-color emissions(or broadband emissions)from a single-phase phosphor with a single activator are an unfamiliar idea compared with those from multi-color-center materials.A single activator that is located in diff...Multi-color emissions(or broadband emissions)from a single-phase phosphor with a single activator are an unfamiliar idea compared with those from multi-color-center materials.A single activator that is located in different crystallographic sites of a single-phase phosphor,however,could lead to multimodal emission peaks for multi-color(or broadband)emissions.The discovery of a single-phase-single-activator-broadband-phosphor is rare,and it is regarded as difficult to accomplish.The present investigation introduces a novel single-phase-single-activator-broadband-phosphor(Ca_(1.624)Sr_(0.376)Si_(5)O_(3)N_(6):Eu^(2+))and provides an in-depth examination of the energy transfer between different crystallographic sites which is the governing mechanism for the broadband emissions.Structural analysis is backed up by density functional theory(DFT)calculations,which validate the structural model of the discovered novel phosphor.Rate-equation modeling is introduced based on particle swarm optimization(PSO)to provide a complete quantitative analysis for the mechanism of the energy transfer.展开更多
The prediction of excitation band edge wavelength(EBEW)and peak emission wavelength(PEW)for Eu^(2+)-activated phosphors is intricate in practice,although a theoretical interpretation has been well established.A data-d...The prediction of excitation band edge wavelength(EBEW)and peak emission wavelength(PEW)for Eu^(2+)-activated phosphors is intricate in practice,although a theoretical interpretation has been well established.A data-driven approach could be of great help for EBEW and PEW prediction.We collected 91 Eu^(2+)-activated phosphors,the host structures of which exhibit a single activator site and the EBEW and PEW of which are available at the critical activator concentration.We extracted 29 descriptors(input features)that implicate the elemental and structural traits of phosphor hosts,and set up an integrated machine-learning(ML)platform consisting of 18 ML algorithms that allowed prediction of the EBEW and PEW as well as the DFT-calculated band gap(Eg).The acquired dataset involving 91 phosphors was insufficient for the 29-input-feature problem and the real-world data collected from the literature have a so-called dirty nature due to inaccurate,unstandardized experiments.Despite an unavoidable paucity of data and the dirty-data problems of real-world data-based ML implementation,we obtained acceptable holdout dataset test results for PEW predications such as R^(2)>0.6,MSE<0.02,and test_R^(2)/training_R^(2)>0.77 for four ML algorithms.The EBEW and E_(g)predictions returned slightly better test results than these PEW examples.展开更多
Deep learning(DL)models trained with synthetic XRD data have never accomplished a satisfactory quantitative XRD analysis for the exact prediction of a constituent-phase fraction in unknown multiphase inorganic compoun...Deep learning(DL)models trained with synthetic XRD data have never accomplished a satisfactory quantitative XRD analysis for the exact prediction of a constituent-phase fraction in unknown multiphase inorganic compounds,although DL-based phase identification has been successful.Here,we report a novel data-driven XRD analysis protocol involving a convolutional neural network(CNN)for exact phase identification and other machine learning(ML)techniques for accurate phase-fraction prediction.A key concept behind this reliable,pragmatic protocol is training with a huge amount of cheap synthetic data and testing with a small amount of expensive real-world experimental data.The protocol was applied to a Li-La-Zr-O quaternary compositional system that involves 218 ICSD-registered inorganic compounds,some of which are known as solid electrolyte materials.Synthetic data-driven XRD analysis has achieved a test accuracy of 96.47% for phase identification and a mean square error(MSE)of 0.0018 and an R2 of 0.9685 for phase-fraction regression.Real-world data tests have led to a phase-identification accuracy of 91.11% and a phase-fraction regression MSE of 0.0024 with an R^(2) of 0.9587.展开更多
The current status of 2D organic–inorganic hybrid perovskites for use in photovoltaic(PV)and light-emitting diode(LED)applications lags far behind their 3D counterparts.Here,we propose a computational strategy for di...The current status of 2D organic–inorganic hybrid perovskites for use in photovoltaic(PV)and light-emitting diode(LED)applications lags far behind their 3D counterparts.Here,we propose a computational strategy for discovering novel perovskites with as few computing resources as possible.A tandem optimization algorithm consisting of an elitism-reinforced nondominated sorting genetic algorithm(NSGA-II)and a multiobjective Bayesian optimization(MOBO)algorithm was used for density functional theory(DFT)calculations.The DFT-calculated band gap and effective mass were taken as objective functions to be optimized,and the constituent molecules and elements of a Ruddlesden–Popper(RP)structure(n=2)were taken as decision variables.Fourteen previously unknown RP perovskite candidates for PV and LED applications were discovered as a result of the NSGA-II/MOBO algorithm.Thereafter,more accurate DFT calculations based on the HSE06 exchange correlation functional and ab initio molecular dynamics(AIMD)were conducted for the discovered 2D perovskites to ensure their validity.展开更多
We report a novel deep learning(DL)method for classifying inorganic compounds using 3D electron density data.We transform Density Functional Theory(DFT)-derived CHGCAR files from the Materials Project(MP)and experimen...We report a novel deep learning(DL)method for classifying inorganic compounds using 3D electron density data.We transform Density Functional Theory(DFT)-derived CHGCAR files from the Materials Project(MP)and experimental data from the Inorganic Crystal Structure Database(ICSD)into point clouds and sparse tensors,optimized for use in DLmodels such as PointNet and Sparse 3DCNN.This approach effectively overcomes the limitations of handling the dense 3D data,a common challenge in DL.Contrasting with traditional 1D or 2D X-ray diffraction(XRD)patterns that necessitate complex reciprocal space analysis,our method utilizes 3D density data for direct interpretation in real lattice space.This shift significantly enhances classification accuracy,outperforming traditional XRD-driven DL methods.We achieve accuracies of 97.28%,90.77%,and 90.10%for crystal system,extinction group,and space group classifications,respectively.Our 3D electron density-based DL approach not only showcases improved accuracy but also contributes a more intuitive and effective framework for materials discovery.展开更多
基金supported by the Creative Materials Discovery Program administered through the NRF of Korea funded by the Ministry of Science,ICT and Future(2015M3D1A1069710)by the Basic Science Research Program(NRF-2014R1A6A1030419,NRF-2021R1A2C1010059)by the Technology Innovation Program(Alchemist Project,20012196,Al based supercritical materials discovery)funded by the Ministry of Trade,Industry&Energy,Korea.
文摘Na_(3)MX_(4) (M=P, Sb and X=S, Se) and its doped analogues are considered as a promising material in room-temperature (RT) Na^(+)-conducting solid electrolytes. Herein, we first report that stoichiometric Na_(3)(W_(x)Si_(x)Sb_(1-2)_(x))S_(4) with no nominal vacancies shows significantly high ionic conductivity at RT (σRT) when compared with Na_(3)SbS_(4). The σRT increases continuously with increases in ‘x’, revealing the highest σRT of 13.2 mS cm^(-1) and the lowest activation of 0.16 eV in cubic Na_(3)(W_(0.2)Si_(0.2)Sb_(0.6))S_(4). Further increases in ‘x’ result in the formation of a glassy phase and a reduction in σRT. The σRT of Na_(3)(W_(0.2)Si_(0.2)Sb_(0.6))S_(4) is the highest in stoichiometric Na_(3)MX_(4) known to date and suggests that the Na^(+) diffusion is influenced by the dopant types as well as structural defects. Ab initio molecular dynamics also reveal the improvement of σRT with increases in ‘x’, but the presence of naturally formed vacancies that are commonly observed in Na_(3)MX_(4). The electronic conductivity of Na_(3)(W_(0.2)Si_(0.2)Sb_(0.6))S_(4) is also low (ca. 10^(-6) mS cm^(-1)). However, the cathodic stability is insufficient when W^(6+) and/or Si^(4+) are doped. Therefore, a solid-state cell (Na_(15)Sn_(4) ∥ TiS_(2)) is fabricated with an interlayer of Na_(3)SbS_(4) between Na_(15)Sn_(4) and Na_(3)(W_(0.2)Si_(0.2)Sb_(0.6))S_(4), and its excellent compatibility with a cathode is demonstrated.
基金supported by Creative Materials Discovery Program through the National Research Foundation of Korea funded by the Ministry of Science,ICT and Future(2015M3D1A1069710)Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Education(NRF-2014R1A6A1030419)the NRF grant funded by the Korea government(2018R1C1B6006943).
文摘Cathode materials in potassium ion batteries(KIBs)generally exhibit low charge storage capabilities when compared with cathode materials implemented in lithium or sodium ion batteries.In this work,K_(0.78)Fe_(1.60)S_(2)is described as a high capacity KIB cathode that exhibits mixed anion/cation redox behaviors during charge/discharge(C/D).When charged to 3.2 V vs.K/K^(+),K^(+)extraction occurs along with simultaneous oxidations of S^(2−)to S_(2)^(2−)and Fe(II)to Fe(III).During subsequent discharge to 1.5 V,this process is reversed,in addition to a further reduction of Fe(II)to Fe(I).After a few C/D cycles,K_(0.78)Fe_(1.6)0S_(2)reversibly delivers 0.69 K^(+)with a capacity of 100.5 mA h g^(−1)(i.e.,K_(0.20)Fe_(1.6)0S_(2)⇆K_(0.89)Fe_(1.6)0S_(2)).The evolution of S_(2)−and Fe(II)valence states along with a lack of discernable changes in crystallographic dimensions clearly confirms the concomitant redox of anions and cations with C/D.Density functional theory calculations also validate the possibility of mixed redox reactions in K_(0.78)Fe_(1.6)0S_(2).Unique structural features of K_(0.78)Fe_(1.60)S_(2)(layers consisting of edge-shared FeS_(4)tetrahedra with partial Fe vacancies)result in high K^(+)diffusion coefficients that are unprecedented(ca.10^(−9)cm^(2)s^(−1)),which contributes to an excellent rate capability(56.3 mA h g^(−1)at 1000 mA^(g−1)vs.100.5 mA h g^(−1)at 20 mA g^(−1)).Nudged elastic band calculations also reveal that the diffusion preferentially occurs along[100]directions with a low activation energy barrier of 0.41 eV.
基金supported by the Creative Materials Discovery Program through the National Research Foundation of Korea(NRF)funded by the the Ministry of Science,ICT,and Future Planning(2015M3D1A1069705)partly by a NRF grant(2018R1C1B6006943).
文摘Multi-color emissions(or broadband emissions)from a single-phase phosphor with a single activator are an unfamiliar idea compared with those from multi-color-center materials.A single activator that is located in different crystallographic sites of a single-phase phosphor,however,could lead to multimodal emission peaks for multi-color(or broadband)emissions.The discovery of a single-phase-single-activator-broadband-phosphor is rare,and it is regarded as difficult to accomplish.The present investigation introduces a novel single-phase-single-activator-broadband-phosphor(Ca_(1.624)Sr_(0.376)Si_(5)O_(3)N_(6):Eu^(2+))and provides an in-depth examination of the energy transfer between different crystallographic sites which is the governing mechanism for the broadband emissions.Structural analysis is backed up by density functional theory(DFT)calculations,which validate the structural model of the discovered novel phosphor.Rate-equation modeling is introduced based on particle swarm optimization(PSO)to provide a complete quantitative analysis for the mechanism of the energy transfer.
基金supported by the Creative Materials Discovery Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Science,ICT,and Future Planning(2015M3D1A1069705),(2021R1A2C1011642)and(2021R1A2C1009144)partly by the Alchemist Project(20012196)Digital manufacturing platform(N0002598)funded by MOTIE,Korea.
文摘The prediction of excitation band edge wavelength(EBEW)and peak emission wavelength(PEW)for Eu^(2+)-activated phosphors is intricate in practice,although a theoretical interpretation has been well established.A data-driven approach could be of great help for EBEW and PEW prediction.We collected 91 Eu^(2+)-activated phosphors,the host structures of which exhibit a single activator site and the EBEW and PEW of which are available at the critical activator concentration.We extracted 29 descriptors(input features)that implicate the elemental and structural traits of phosphor hosts,and set up an integrated machine-learning(ML)platform consisting of 18 ML algorithms that allowed prediction of the EBEW and PEW as well as the DFT-calculated band gap(Eg).The acquired dataset involving 91 phosphors was insufficient for the 29-input-feature problem and the real-world data collected from the literature have a so-called dirty nature due to inaccurate,unstandardized experiments.Despite an unavoidable paucity of data and the dirty-data problems of real-world data-based ML implementation,we obtained acceptable holdout dataset test results for PEW predications such as R^(2)>0.6,MSE<0.02,and test_R^(2)/training_R^(2)>0.77 for four ML algorithms.The EBEW and E_(g)predictions returned slightly better test results than these PEW examples.
基金supported by the Creative Materials Discovery Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Science,ICT,and Future Planning(2015M3D1A1069705 and 2019H1D8A2106002)partly by the Alchemist Project(20012196),and the Digital manufacturing platform(N0002598)funded by MOTIE,Korea.
文摘Deep learning(DL)models trained with synthetic XRD data have never accomplished a satisfactory quantitative XRD analysis for the exact prediction of a constituent-phase fraction in unknown multiphase inorganic compounds,although DL-based phase identification has been successful.Here,we report a novel data-driven XRD analysis protocol involving a convolutional neural network(CNN)for exact phase identification and other machine learning(ML)techniques for accurate phase-fraction prediction.A key concept behind this reliable,pragmatic protocol is training with a huge amount of cheap synthetic data and testing with a small amount of expensive real-world experimental data.The protocol was applied to a Li-La-Zr-O quaternary compositional system that involves 218 ICSD-registered inorganic compounds,some of which are known as solid electrolyte materials.Synthetic data-driven XRD analysis has achieved a test accuracy of 96.47% for phase identification and a mean square error(MSE)of 0.0018 and an R2 of 0.9685 for phase-fraction regression.Real-world data tests have led to a phase-identification accuracy of 91.11% and a phase-fraction regression MSE of 0.0024 with an R^(2) of 0.9587.
基金This research was supported by the Creative Materials Discovery Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Science,ICT,and Future Planning(2021R1A2C1009144),(2021R1A2C1011642)and(2021M3A7C2089778).
文摘The current status of 2D organic–inorganic hybrid perovskites for use in photovoltaic(PV)and light-emitting diode(LED)applications lags far behind their 3D counterparts.Here,we propose a computational strategy for discovering novel perovskites with as few computing resources as possible.A tandem optimization algorithm consisting of an elitism-reinforced nondominated sorting genetic algorithm(NSGA-II)and a multiobjective Bayesian optimization(MOBO)algorithm was used for density functional theory(DFT)calculations.The DFT-calculated band gap and effective mass were taken as objective functions to be optimized,and the constituent molecules and elements of a Ruddlesden–Popper(RP)structure(n=2)were taken as decision variables.Fourteen previously unknown RP perovskite candidates for PV and LED applications were discovered as a result of the NSGA-II/MOBO algorithm.Thereafter,more accurate DFT calculations based on the HSE06 exchange correlation functional and ab initio molecular dynamics(AIMD)were conducted for the discovered 2D perovskites to ensure their validity.
基金supported by the Alchemist Project(20012196)funded by MOTIE,Koreapartly by the National Research Foundation of Korea(NRF)(2021R1A2C1009144 and RS-2024-00446825).
文摘We report a novel deep learning(DL)method for classifying inorganic compounds using 3D electron density data.We transform Density Functional Theory(DFT)-derived CHGCAR files from the Materials Project(MP)and experimental data from the Inorganic Crystal Structure Database(ICSD)into point clouds and sparse tensors,optimized for use in DLmodels such as PointNet and Sparse 3DCNN.This approach effectively overcomes the limitations of handling the dense 3D data,a common challenge in DL.Contrasting with traditional 1D or 2D X-ray diffraction(XRD)patterns that necessitate complex reciprocal space analysis,our method utilizes 3D density data for direct interpretation in real lattice space.This shift significantly enhances classification accuracy,outperforming traditional XRD-driven DL methods.We achieve accuracies of 97.28%,90.77%,and 90.10%for crystal system,extinction group,and space group classifications,respectively.Our 3D electron density-based DL approach not only showcases improved accuracy but also contributes a more intuitive and effective framework for materials discovery.
