Accelerated design of hard-coating materials requires state-of-the-art computational tools,which include data-driven techniques,building databases,and training machine learning models.We develop a heavily automated hi...Accelerated design of hard-coating materials requires state-of-the-art computational tools,which include data-driven techniques,building databases,and training machine learning models.We develop a heavily automated high-throughput workflow to build a database of industrially relevant hard-coating materials,such as binary and ternary nitrides.We use the high-throughput toolkit to automate the density functional theory calculation workflow.We present results,including elastic constants that are a key parameter determining mechanical properties of hard-coatings,for X_(1−x)Y_(x)N ternary nitrides,where X,Y∈{Al,Ti,Zr,Hf}and fraction x=0,1/4,1/2,3/4,1.We also explore ways for machine learning to support and complement the designed databases.We find that the crystal graph convolutional neural network trained on ordered lattices has sufficient accuracy for the disordered nitrides,suggesting that existing databases provide important data for predicting mechanical properties of qualitatively different types of materials,in our case disordered hard-coating alloys.展开更多
The archetypal 3d Mott insulator hematite,Fe_(2)O_(3),is one of the basic oxide components playing an important role in mineralogy of Earth’s lower mantle.Its high pressure-temperature behavior,such as the electronic...The archetypal 3d Mott insulator hematite,Fe_(2)O_(3),is one of the basic oxide components playing an important role in mineralogy of Earth’s lower mantle.Its high pressure-temperature behavior,such as the electronic properties,equation of state,and phase stability is of fundamental importance for understanding the properties and evolution of the Earth’s interior.Here,we study the electronic structure,magnetic state,and lattice stability of Fe_(2)O_(3)at ultra-high pressures using the density functional plus dynamical mean-field theory(DFT+DMFT)approach.In the vicinity of a Mott transition,Fe_(2)O_(3) is found to exhibit a series of complex electronic,magnetic,and structural transformations.In particular,it makes a phase transition to a metal with a post-perovskite crystal structure and site-selective local moments upon compression above 75 GPa.We show that the site-selective phase transition is accompanied by a charge disproportionation of Fe ions,with Fe^(3±δ)and δ~0.05–0.09,implying a complex interplay between electronic correlations and the lattice.Our results suggest that site-selective local moments in Fe_(2)O_(3) persist up to ultra-high pressures of~200–250 GPa,i.e.,sufficiently above the core-mantle boundary.The latter can have important consequences for understanding of the velocity and density anomalies in the Earth’s lower mantle.展开更多
Point defect research in semiconductors has gained remarkable new momentum due to the identification of special point defects that can implement qubits and single photon emitters with unique characteristics.Indeed,the...Point defect research in semiconductors has gained remarkable new momentum due to the identification of special point defects that can implement qubits and single photon emitters with unique characteristics.Indeed,these implementations are among the few alternatives for quantum technologies that may operate even at room temperature,and therefore discoveries and characterization of novel point defects may highly facilitate future solid state quantum technologies.First principles calculations play an important role in point defect research,since they provide a direct,extended insight into the formation of the defect states.In the last decades,considerable efforts have been made to calculate spin-dependent properties of point defects from first principles.The developed methods have already demonstrated their essential role in quantitative understanding of the physics and application of point defect qubits.Here,we review and discuss accuracy aspects of these novel ab initio methods and report on their most relevant applications for existing point defect qubits in semiconductors.We pay attention to the advantages and limitations of the methodological solutions and highlight additional developments that are expected in the near future.Moreover,we discuss the opportunity of a systematic search for potential point defect qubits,as well as the possible development of predictive spin dynamic simulations facilitated by ab initio calculations of spin-dependent quantities.展开更多
基金The authors gratefully acknowledge financial support from the Competence Center Functional Nanoscale Materials(FunMat-II)(Vinnova Grant No.2016-05156)Support from the Knut and Alice Wallenberg Foundation(Wallenberg Scholar Grant No.KAW-2018.0194)+3 种基金the Swedish Government Strategic Research Areas in Materials Science on Functional Materials at Linköping University(Faculty Grant SFO-Mat-LiU No.200900971)SeRC is gratefully acknowledged.Theoretical analysis of results of first-principles calculations was supported by the Russian Science Foundation(Project No.18-12-00492)R.A.acknowledges support from the Swedish Research Council(VR)Grant No.2020-05402 and the Swedish e-Science Centre(SeRC)The computations were enabled by resources provided by the Swedish National Infrastructure for Computing(SNIC),partially funded by the Swedish Research Council through grant agreement no.2018-05973。
文摘Accelerated design of hard-coating materials requires state-of-the-art computational tools,which include data-driven techniques,building databases,and training machine learning models.We develop a heavily automated high-throughput workflow to build a database of industrially relevant hard-coating materials,such as binary and ternary nitrides.We use the high-throughput toolkit to automate the density functional theory calculation workflow.We present results,including elastic constants that are a key parameter determining mechanical properties of hard-coatings,for X_(1−x)Y_(x)N ternary nitrides,where X,Y∈{Al,Ti,Zr,Hf}and fraction x=0,1/4,1/2,3/4,1.We also explore ways for machine learning to support and complement the designed databases.We find that the crystal graph convolutional neural network trained on ordered lattices has sufficient accuracy for the disordered nitrides,suggesting that existing databases provide important data for predicting mechanical properties of qualitatively different types of materials,in our case disordered hard-coating alloys.
基金Theoretical analysis of structural properties was supported by the Russian Science Foundation(Project No.18-12-00492)Support provided by the Swedish Research Council Project No.2015-04391,the Swedish Government Strategic Research Areas in Materials Science on Functional Materials at Linköping University(Faculty Grant SFOMat-LiU No.2009-00971)+1 种基金the Swedish e-Science Research Centre(SeRC)is gratefully acknowledgedThis research was supported in part by Israeli Science Foundation Grant #1189/14 and #1552/18.
文摘The archetypal 3d Mott insulator hematite,Fe_(2)O_(3),is one of the basic oxide components playing an important role in mineralogy of Earth’s lower mantle.Its high pressure-temperature behavior,such as the electronic properties,equation of state,and phase stability is of fundamental importance for understanding the properties and evolution of the Earth’s interior.Here,we study the electronic structure,magnetic state,and lattice stability of Fe_(2)O_(3)at ultra-high pressures using the density functional plus dynamical mean-field theory(DFT+DMFT)approach.In the vicinity of a Mott transition,Fe_(2)O_(3) is found to exhibit a series of complex electronic,magnetic,and structural transformations.In particular,it makes a phase transition to a metal with a post-perovskite crystal structure and site-selective local moments upon compression above 75 GPa.We show that the site-selective phase transition is accompanied by a charge disproportionation of Fe ions,with Fe^(3±δ)and δ~0.05–0.09,implying a complex interplay between electronic correlations and the lattice.Our results suggest that site-selective local moments in Fe_(2)O_(3) persist up to ultra-high pressures of~200–250 GPa,i.e.,sufficiently above the core-mantle boundary.The latter can have important consequences for understanding of the velocity and density anomalies in the Earth’s lower mantle.
基金Support from the Swedish Government Strategic Research Areas in Materials Science on Functional Materials at Linköping University(Faculty Grant SFO-Mat-LiU No.2009-00971)Knut&Alice Wallenberg Foundation New States of Matter 2014-2019(COTXS)is gratefully acknowledged.Analysis of first-principles calculations of defect properties was supported by the Ministry of Education and Science of the Russian Federation(Grant no.14.Y26.31.0005).Applications of the model Hamiltonians were supported by the Ministry of Education and Science of the Russian Federation in the framework of Increase Competitiveness Program of NUST“MISIS”(No.K2-2017-080)implemented by a governmental decree dated 16 March 2013,no.211.A.G.acknowledges the support from the National Research Development and Innovation Office of Hungary(NKFIH)within the Quantum Technology National Excellence Program(Project no.2017-1.2.1-NKP-2017-00001)the EU QuantERA projects QMagine and Nanospin(NKFIH Grant Nos.127889 and 127902,respectively),and the EU H2020 ASTERIQS project.
文摘Point defect research in semiconductors has gained remarkable new momentum due to the identification of special point defects that can implement qubits and single photon emitters with unique characteristics.Indeed,these implementations are among the few alternatives for quantum technologies that may operate even at room temperature,and therefore discoveries and characterization of novel point defects may highly facilitate future solid state quantum technologies.First principles calculations play an important role in point defect research,since they provide a direct,extended insight into the formation of the defect states.In the last decades,considerable efforts have been made to calculate spin-dependent properties of point defects from first principles.The developed methods have already demonstrated their essential role in quantitative understanding of the physics and application of point defect qubits.Here,we review and discuss accuracy aspects of these novel ab initio methods and report on their most relevant applications for existing point defect qubits in semiconductors.We pay attention to the advantages and limitations of the methodological solutions and highlight additional developments that are expected in the near future.Moreover,we discuss the opportunity of a systematic search for potential point defect qubits,as well as the possible development of predictive spin dynamic simulations facilitated by ab initio calculations of spin-dependent quantities.
