Characteristic shock effects in quartz serve as a key indicator of historic impacts at geologic sites.Despite this geologic significance,atomistic details of structural transformations of quartz under high pressure an...Characteristic shock effects in quartz serve as a key indicator of historic impacts at geologic sites.Despite this geologic significance,atomistic details of structural transformations of quartz under high pressure and shock compression remain poorly understood.This ambiguity is evidenced by conflicting experimental observations of both amorphization and transitions to crystalline polymorphs.Utilizing a newly developed machine-learning interatomic potential,we examine the response ofα-quartz to shock compression with a peak pressure of 56 GPa over nanosecond timescales.We observe initial amorphization of quartz before crystallization into a d-NiAs-structured silica phase with disorder on the silicon sublattice,accompanied by the formation of domains with partial order of silicon.Investigating a variety of strain conditions of quartz enables us to identify nonhydrostatic stress and strain states that allow for direct diffusionless transformation to rosiaitestructured silica.展开更多
Silica(SiO_(2))is an abundant material with a wide range of applications.Despite much progress,the atomistic modelling of the different forms of silica has remained a challenge.Here we show that by combining density-f...Silica(SiO_(2))is an abundant material with a wide range of applications.Despite much progress,the atomistic modelling of the different forms of silica has remained a challenge.Here we show that by combining density-functional theory at the SCAN functional level with machine-learning-based interatomic potential fitting,a range of condensed phases of silica can be accurately described.We present a Gaussian approximation potential model that achieves high accuracy for the thermodynamic properties of the crystalline phases,and we compare its performance(and performance–cost trade-off)with that of multiple empirically fitted interatomic potentials for silica.We also include amorphous phases,assessing the ability of the potentials to describe structures of melt-quenched glassy silica,their energetic stability,and the high-pressure structural transition to a mainly sixfold-coordinated phase.We suggest that rather than standing on their own,machine-learned potentials for silica may be used in conjunction with suitable empirical models,each having a distinct role and complementing the other,by combining the advantages of the long simulation times afforded by empirical potentials and the near-quantum-mechanical accuracy of machine-learned potentials.This way,our work is expected to advance atomistic simulations of this key material and to benefit further computational studies in the field.展开更多
Designing the microstructure of Fe-Ni permalloy produced by additive manufacturing(AM)opens new avenues to tailor its magnetic properties.Yet,AM-produced parts suffer from spatially inhomogeneous thermal-mechanical an...Designing the microstructure of Fe-Ni permalloy produced by additive manufacturing(AM)opens new avenues to tailor its magnetic properties.Yet,AM-produced parts suffer from spatially inhomogeneous thermal-mechanical and magnetic responses,which are less investigated in terms of process modeling and simulations.We present a powder-resolved multiphysics-multiscale simulation scheme for describing magnetic hysteresis in AM-produced material,explicitly considering the coupled thermal-structural evolution with associated thermo-elasto-plastic behaviors and chemical order-disorder transitions.The residual stress is identified as the key thread in connecting the physical processes and phenomena across scales.By employing this scheme,we investigate the dependence of the fusion zone size,the residual stress and plastic strain,and the magnetic hysteresis of AM-produced Fe_(21.5)Ni_(78.5) on beam power and scan speed.Simulation results also suggest a phenomenological relation between magnetic coercivity and average residual stress,which can guide the magnetic hysteresis design of soft magnetic materials by choosing appropriate processing parameters.展开更多
Creation of a partially filled intermediate band in a photovoltaic absorber material is an appealing concept for increasing the quantum efficiency of solar cells.Recently,we showed that formation of a partially filled...Creation of a partially filled intermediate band in a photovoltaic absorber material is an appealing concept for increasing the quantum efficiency of solar cells.Recently,we showed that formation of a partially filled intermediate band through doping a host semiconductor with a transition metal dopant is hindered by the strongly correlated nature of d-electrons and the antecedent Jahn–Teller distortion,as we have previously reported.In present work,we take a step forward and study the delocalization of a filled(valence-like)intermediate band throughout the lattice:a case study of Ti-and Nb-doped In_(2)S_(3).By means of hybrid density functional calculations,we present extensive analysis on structural properties and interactions leading to electronic characteristics of Ti-and Nb-doped In_(2)S_(3).展开更多
We present a comprehensive and user-friendly framework built upon the pyiron integrated development environment(IDE),enabling researchers to perform the entire Machine Learning Potential(MLP)development cycle consisti...We present a comprehensive and user-friendly framework built upon the pyiron integrated development environment(IDE),enabling researchers to perform the entire Machine Learning Potential(MLP)development cycle consisting of(i)creating systematic DFT databases,(ii)fitting the Density Functional Theory(DFT)data to empirical potentials orMLPs,and(iii)validating the potentials in a largely automatic approach.The power and performance of this framework are demonstrated for three conceptually very different classes of interatomic potentials:an empirical potential(embedded atom method-EAM),neural networks(high-dimensional neural network potentials-HDNNP)and expansions in basis sets(atomic cluster expansion-ACE).As an advanced example for validation and application,we show the computation of a binary composition-temperature phase diagram for Al-Li,a technologically important lightweight alloy system with applications in the aerospace industry.展开更多
基金support by the Deutsche Forschungsgemeinschaft(DFG,Grant no.405621137,405621160)。
文摘Characteristic shock effects in quartz serve as a key indicator of historic impacts at geologic sites.Despite this geologic significance,atomistic details of structural transformations of quartz under high pressure and shock compression remain poorly understood.This ambiguity is evidenced by conflicting experimental observations of both amorphization and transitions to crystalline polymorphs.Utilizing a newly developed machine-learning interatomic potential,we examine the response ofα-quartz to shock compression with a peak pressure of 56 GPa over nanosecond timescales.We observe initial amorphization of quartz before crystallization into a d-NiAs-structured silica phase with disorder on the silicon sublattice,accompanied by the formation of domains with partial order of silicon.Investigating a variety of strain conditions of quartz enables us to identify nonhydrostatic stress and strain states that allow for direct diffusionless transformation to rosiaitestructured silica.
基金The research was supported by the Bundesministerium für Bildung und Forschung(BMBF)within the project FESTBATT under Grant No.03XP0174AJ.R.and L.C.E.acknowledge support from the Deutsche Forschungsgemeinschaft(DFG,Grant no.RO 4542/4-1 and STU 611/5-1).
文摘Silica(SiO_(2))is an abundant material with a wide range of applications.Despite much progress,the atomistic modelling of the different forms of silica has remained a challenge.Here we show that by combining density-functional theory at the SCAN functional level with machine-learning-based interatomic potential fitting,a range of condensed phases of silica can be accurately described.We present a Gaussian approximation potential model that achieves high accuracy for the thermodynamic properties of the crystalline phases,and we compare its performance(and performance–cost trade-off)with that of multiple empirically fitted interatomic potentials for silica.We also include amorphous phases,assessing the ability of the potentials to describe structures of melt-quenched glassy silica,their energetic stability,and the high-pressure structural transition to a mainly sixfold-coordinated phase.We suggest that rather than standing on their own,machine-learned potentials for silica may be used in conjunction with suitable empirical models,each having a distinct role and complementing the other,by combining the advantages of the long simulation times afforded by empirical potentials and the near-quantum-mechanical accuracy of machine-learned potentials.This way,our work is expected to advance atomistic simulations of this key material and to benefit further computational studies in the field.
基金B.-X.X.acknowledges the financial support of German Science Foundation(DFG)in the framework of the Collaborative Research Centre Transregio 270(CRC-TRR 270,project number 405553726,sub-projects A06,B07,Z-INF)and 361(CRC-TRR 361,project number 492661287,sub-projects A05)the Research Training Groups 2561(GRK 2561,project number 413956820,sub-project A4)+2 种基金the Priority Program 2256(SPP 2256,project number 441153493)and 2122(SPP 2122,project number 493889809)X.Z.acknowledges the support from Sichuan Science and Technology Program(project number 2023NSFSC0910)Fundamental Research Funds for the Central Universities of China(project number 2023SCU12103).The authors acknowl-edge the support by the Open Access Publishing Fund of Technische UniversitäDarmstadt.The authors also greatly appreciate the access to the Lichtenberg II High-Performance Computer(HPC)and the technique supports from the HHLR,Technische Universität Darmstadt,and the GPU Cluster from the CRC-TRR 270 sub-project Z-INF.The computating time on the HPC is granted by the NHR4CES Resource Allocation Board under the project“special00007”.Y.Y.also highly thanks the Master’s student Akinola Ayodeji Clement for helping with SLS and thermo-elasto-plastic simulations.
文摘Designing the microstructure of Fe-Ni permalloy produced by additive manufacturing(AM)opens new avenues to tailor its magnetic properties.Yet,AM-produced parts suffer from spatially inhomogeneous thermal-mechanical and magnetic responses,which are less investigated in terms of process modeling and simulations.We present a powder-resolved multiphysics-multiscale simulation scheme for describing magnetic hysteresis in AM-produced material,explicitly considering the coupled thermal-structural evolution with associated thermo-elasto-plastic behaviors and chemical order-disorder transitions.The residual stress is identified as the key thread in connecting the physical processes and phenomena across scales.By employing this scheme,we investigate the dependence of the fusion zone size,the residual stress and plastic strain,and the magnetic hysteresis of AM-produced Fe_(21.5)Ni_(78.5) on beam power and scan speed.Simulation results also suggest a phenomenological relation between magnetic coercivity and average residual stress,which can guide the magnetic hysteresis design of soft magnetic materials by choosing appropriate processing parameters.
基金This work has been financially supported by Deutsche Forschungsgemeinschaft(DFG)under project No.414750661The computing time was provided by Jülich Supercomputing Center(Project No.HDA30)and Lichtenberg HPC computer resources at TU Darmstadt.
文摘Creation of a partially filled intermediate band in a photovoltaic absorber material is an appealing concept for increasing the quantum efficiency of solar cells.Recently,we showed that formation of a partially filled intermediate band through doping a host semiconductor with a transition metal dopant is hindered by the strongly correlated nature of d-electrons and the antecedent Jahn–Teller distortion,as we have previously reported.In present work,we take a step forward and study the delocalization of a filled(valence-like)intermediate band throughout the lattice:a case study of Ti-and Nb-doped In_(2)S_(3).By means of hybrid density functional calculations,we present extensive analysis on structural properties and interactions leading to electronic characteristics of Ti-and Nb-doped In_(2)S_(3).
基金The workflows,potentials,and results presented here were obtained in the framework of the POTENTIALS collaboration and scientific network“Assessment of atomistic simulations”with funding from the German Science Foundation(DFG)(grant number 405602047)S.M.acknowledges funding by the Deutsche Forschungsgemeinschaft(DFG,German Research Foundation)under the National Research Data Infrastructure-NFDI 38/1-project number 460247524+5 种基金J.B.acknowledges funding by the DFG(project number 405479457 as part of PAK 965/1)A.K.acknowledges funding by the Studienstiftung des Deutschen Volkes(doctoral scholarship)N.L.and J.R.acknowledge funding by the Deutsche Forschungsgemeinschaft(DFG,German Research Foundation)under grant number 405621137K.A.acknowledges funding from the the DFG undergrant number 405621160M.M.and R.D.acknowledge funding by the German Science Foundation(DFG),projects 405621081 and 405621217.R.D.and Y.L.acknowledge computation time by Center for Interface-Dominated High Performance Materials(ZGH)at Ruhr-Universität Bochum,GermanyJ.J.and J.N.acknowledge funding by the DFG under grant number 405621217.M.P.and J.N.acknowledge funding from the DFG under grant number 405621160.
文摘We present a comprehensive and user-friendly framework built upon the pyiron integrated development environment(IDE),enabling researchers to perform the entire Machine Learning Potential(MLP)development cycle consisting of(i)creating systematic DFT databases,(ii)fitting the Density Functional Theory(DFT)data to empirical potentials orMLPs,and(iii)validating the potentials in a largely automatic approach.The power and performance of this framework are demonstrated for three conceptually very different classes of interatomic potentials:an empirical potential(embedded atom method-EAM),neural networks(high-dimensional neural network potentials-HDNNP)and expansions in basis sets(atomic cluster expansion-ACE).As an advanced example for validation and application,we show the computation of a binary composition-temperature phase diagram for Al-Li,a technologically important lightweight alloy system with applications in the aerospace industry.
