We introduce an automated,flexible framework(aiida-hubbard)to self-consistently calculate Hubbard U and V parameters from first-principles.By leveraging density-functional perturbation theory,the computation of the Hu...We introduce an automated,flexible framework(aiida-hubbard)to self-consistently calculate Hubbard U and V parameters from first-principles.By leveraging density-functional perturbation theory,the computation of the Hubbard parameters is efficiently parallelized using multiple concurrent and inexpensive primitive cell calculations.Furthermore,the intersite V parameters are defined on-the-fly during the iterative procedure to account for atomic relaxations and diverse coordination environments.We devise a novel,code-agnostic data structure to store Hubbard related information together with the atomistic structure,to enhance the reproducibility of Hubbard-corrected calculations.We demonstrate the scalability and reliability of the framework by computing in high-throughput fashion the self-consistent onsite U and intersite V parameters for 115 Li-containing bulk solids with up to 32 atoms in the unit cell.Our analysis of the Hubbard parameters calculated reveals a significant correlation of the onsite U values on the oxidation state and coordination environment of the atom on which the Hubbard manifold is centered,while intersite V values exhibit a general decay with increasing interatomic distance.We find,e.g.,that the numerical values of U for the 3d orbitals of Fe and Mn can vary up to 3 eV and 6 eV,respectively;their distribution is characterized by typical shifts of about 0.5 eV and 1.0 eV upon change in oxidation state,or local coordination environment.For the intersite V a narrower spread is found,with values ranging between 0.2 eV and 1.6 eV when considering transition metal and oxygen interactions.This framework paves the way for the exploration of redox materials chemistry and high-throughput screening of d and f compounds across diverse research areas,including the discovery and design of novel energy storage materials,as well as other technologically-relevant applications.展开更多
Spin excitations play a fundamental role in understanding magnetic properties of materials,and have significant technological implications for magnonic devices.However,accurately modeling these in transition-metal and...Spin excitations play a fundamental role in understanding magnetic properties of materials,and have significant technological implications for magnonic devices.However,accurately modeling these in transition-metal and rare-earth compounds remains a formidable challenge.Here,we present a fully first-principles approach for calculating spin-wave spectra based on time-dependent(TD)density-functional perturbation theory(DFPT),using nonempirical Hubbard functionals.This approach is implemented in a general noncollinear formulation,enabling the study of magnons in both collinear and noncollinear magnetic systems.Unlike methods that rely on empirical Hubbard U parameters to describe the ground state,and Heisenberg Hamiltonians for describing magnetic excitations,the methodology developed here probes directly the dynamical spin susceptibility(efficiently evaluated with TDDFPT throught the Liouville-Lanczos approach),and treats the linear variation of the Hubbard augmentation(in itself calculated non-empirically)in full at a self-consistent level.Furthermore,the method satisfies the Goldstone condition without requiring empirical rescaling of the exchange-correlation kernel or explicit enforcement of sum rules,in contrast to existing state-of-the-art techniques.We benchmark the novel computational scheme on prototypical transition-metal monoxides NiO and MnO,showing remarkable agreement with experiments and highlighting the fundamental role of these newly implemented Hubbard corrections.The method holds great promise for describing collective spin excitations in complex materials containing localized electronic states.展开更多
Density-functional theory with extended Hubbard functionals(DFT+U+V)provides a robust framework to accurately describe complex materials containing transition-metal or rare-earth elements.It does so by mitigating self...Density-functional theory with extended Hubbard functionals(DFT+U+V)provides a robust framework to accurately describe complex materials containing transition-metal or rare-earth elements.It does so by mitigating self-interaction errors inherent to semi-local functionals which are particularly pronounced in systems with partially-filled d and f electronic states.However,achieving accuracy in this approach hinges upon the accurate determination of the on-site U and inter-site V Hubbard parameters.In practice,these are obtained either by semi-empirical tuning,requiring prior knowledge,or,more correctly,by using predictive but expensive first-principles calculations.Here,we present a machine learning model based on equivariant neural networks which uses atomic occupation matrices as descriptors,directly capturing the electronic structure,local chemical environment,and oxidation states of the system at hand.We target here the prediction of Hubbard parameters computed self-consistently with iterative linear-response calculations,as implemented in density-functional perturbation theory(DFPT),and structural relaxations.Remarkably,when trained on data from 12 materials spanning various crystal structures and compositions,our model achieves mean absolute relative errors of 3%and 5%for Hubbard U and V parameters,respectively.By circumventing computationally expensive DFT or DFPT self-consistent protocols,our model significantly expedites the prediction of Hubbard parameters with negligible computational overhead,while approaching the accuracy of DFPT.Moreover,owing to its robust transferability,the model facilitates accelerated materials discovery and design via high-throughput calculations,with relevance for various technological applications.展开更多
基金support from the Deutsche Forschungsgemeinschaft(DFG)under Germany’s Excellence Strategy(EXC 2077,No.390741603,University Allowance,University of Bremen),Lucio Colombi Ciacchi,the host of the“U Bremen Excellence Chair Program”C.M.and E.M acknowledge funding by MaX“Materials Design at the Exascale”,a Center of Excellence co-funded by the European High Performance Computing Joint Undertaking(JU)and participating countries under grant agreement No.101093374+4 种基金M.B.acknowledges funding by the European Centre of Excellence MaX“Materials design at the Exascale”(grant no.824143)and by the SwissTwins project,funded by the Swiss State Secretariat for Education,Research and Innovation(SERI)I.T.acknowledges funding by the Swiss National Science Foundation(grant no.200021-227641)We acknowledge support by the NCCR MARVEL,a National Centre of Competence in Research,funded by the Swiss National Science Foundation(Grant number 205602)This work was supported by a grant from the Swiss National Supercomputing Centre(CSCS)under project ID 465000416(LUMI-G).We thank Julian Geiger,Gabriel Joalland,Austin Zadoks and Timo Reents for useful discussions and feedbacks.
文摘We introduce an automated,flexible framework(aiida-hubbard)to self-consistently calculate Hubbard U and V parameters from first-principles.By leveraging density-functional perturbation theory,the computation of the Hubbard parameters is efficiently parallelized using multiple concurrent and inexpensive primitive cell calculations.Furthermore,the intersite V parameters are defined on-the-fly during the iterative procedure to account for atomic relaxations and diverse coordination environments.We devise a novel,code-agnostic data structure to store Hubbard related information together with the atomistic structure,to enhance the reproducibility of Hubbard-corrected calculations.We demonstrate the scalability and reliability of the framework by computing in high-throughput fashion the self-consistent onsite U and intersite V parameters for 115 Li-containing bulk solids with up to 32 atoms in the unit cell.Our analysis of the Hubbard parameters calculated reveals a significant correlation of the onsite U values on the oxidation state and coordination environment of the atom on which the Hubbard manifold is centered,while intersite V values exhibit a general decay with increasing interatomic distance.We find,e.g.,that the numerical values of U for the 3d orbitals of Fe and Mn can vary up to 3 eV and 6 eV,respectively;their distribution is characterized by typical shifts of about 0.5 eV and 1.0 eV upon change in oxidation state,or local coordination environment.For the intersite V a narrower spread is found,with values ranging between 0.2 eV and 1.6 eV when considering transition metal and oxygen interactions.This framework paves the way for the exploration of redox materials chemistry and high-throughput screening of d and f compounds across diverse research areas,including the discovery and design of novel energy storage materials,as well as other technologically-relevant applications.
基金support by the NCCR MARVEL,a National Centre of Competence in Research,funded by the Swiss National Science Foundation(Grant number 205602)the Fellowship from the EPFL QSE Center“Many-body neural simulations of quantum materials”(Grant number 10060)supported by a grant from the Swiss National Supercomputing Centre(CSCS)under project ID s1073 and mr33(Piz Daint).
文摘Spin excitations play a fundamental role in understanding magnetic properties of materials,and have significant technological implications for magnonic devices.However,accurately modeling these in transition-metal and rare-earth compounds remains a formidable challenge.Here,we present a fully first-principles approach for calculating spin-wave spectra based on time-dependent(TD)density-functional perturbation theory(DFPT),using nonempirical Hubbard functionals.This approach is implemented in a general noncollinear formulation,enabling the study of magnons in both collinear and noncollinear magnetic systems.Unlike methods that rely on empirical Hubbard U parameters to describe the ground state,and Heisenberg Hamiltonians for describing magnetic excitations,the methodology developed here probes directly the dynamical spin susceptibility(efficiently evaluated with TDDFPT throught the Liouville-Lanczos approach),and treats the linear variation of the Hubbard augmentation(in itself calculated non-empirically)in full at a self-consistent level.Furthermore,the method satisfies the Goldstone condition without requiring empirical rescaling of the exchange-correlation kernel or explicit enforcement of sum rules,in contrast to existing state-of-the-art techniques.We benchmark the novel computational scheme on prototypical transition-metal monoxides NiO and MnO,showing remarkable agreement with experiments and highlighting the fundamental role of these newly implemented Hubbard corrections.The method holds great promise for describing collective spin excitations in complex materials containing localized electronic states.
基金support by the NCCR MARVEL,a National Centre of Competence in Research,funded by the Swiss National Science Foundation(Grant number 205602)supported by a grant from the Swiss National Supercomputing Centre(CSCS)under project ID s1073(Piz Daint)and ID 465000416(LUMI-G)supported by MIAI@Grenoble Alpes,(ANR-19-P3IA-0003).
文摘Density-functional theory with extended Hubbard functionals(DFT+U+V)provides a robust framework to accurately describe complex materials containing transition-metal or rare-earth elements.It does so by mitigating self-interaction errors inherent to semi-local functionals which are particularly pronounced in systems with partially-filled d and f electronic states.However,achieving accuracy in this approach hinges upon the accurate determination of the on-site U and inter-site V Hubbard parameters.In practice,these are obtained either by semi-empirical tuning,requiring prior knowledge,or,more correctly,by using predictive but expensive first-principles calculations.Here,we present a machine learning model based on equivariant neural networks which uses atomic occupation matrices as descriptors,directly capturing the electronic structure,local chemical environment,and oxidation states of the system at hand.We target here the prediction of Hubbard parameters computed self-consistently with iterative linear-response calculations,as implemented in density-functional perturbation theory(DFPT),and structural relaxations.Remarkably,when trained on data from 12 materials spanning various crystal structures and compositions,our model achieves mean absolute relative errors of 3%and 5%for Hubbard U and V parameters,respectively.By circumventing computationally expensive DFT or DFPT self-consistent protocols,our model significantly expedites the prediction of Hubbard parameters with negligible computational overhead,while approaching the accuracy of DFPT.Moreover,owing to its robust transferability,the model facilitates accelerated materials discovery and design via high-throughput calculations,with relevance for various technological applications.
