Graph neural networks are attractive for learning properties of atomic structures thanks to their intuitive graph encoding of atoms and bonds.However,conventional encoding does not include angular information,which is...Graph neural networks are attractive for learning properties of atomic structures thanks to their intuitive graph encoding of atoms and bonds.However,conventional encoding does not include angular information,which is critical for describing atomic arrangements in disordered systems.In this work,we extend the recently proposed ALIGNN(Atomistic Line Graph Neural Network)encoding,which incorporates bond angles,to also include dihedral angles(ALIGNN-d).This simple extension leads to a memory-efficient graph representation that captures the complete geometry of atomic structures.ALIGNN-d is applied to predict the infrared optical response of dynamically disordered Cu(II)aqua complexes,leveraging the intrinsic interpretability to elucidate the relative contributions of individual structural components.Bond and dihedral angles are found to be critical contributors to the fine structure of the absorption response,with distortions that represent transitions between more common geometries exhibiting the strongest absorption intensity.Future directions for further development of ALIGNN-d are discussed.展开更多
Although multiple oxide-based solid electrolyte materials with intrinsically high ionic conductivities have emerged,practical processing and synthesis routes introduce grain boundaries and other interfaces that can pe...Although multiple oxide-based solid electrolyte materials with intrinsically high ionic conductivities have emerged,practical processing and synthesis routes introduce grain boundaries and other interfaces that can perturb primary conduction channels.To directly probe these effects,we demonstrate an efficient and general mesoscopic computational method capable of predicting effective ionic conductivity through a complex polycrystalline oxide-based solid electrolyte microstructure without relying on simplified equivalent circuit description.We parameterize the framework for Li_(7-x)La_(3)Zr_(2)0_(12)(LLZO)gamet solid electrolyte by combining synthetic microstructures from phase-field simulations with diffusivities from molecular dynamics simulations of ordered and disordered systems.Systematically designed simulations reveal an interdependence between atomistic and mesoscopic microstructural impacts on the effective ionic conductivity of polycrystalline LLZO,quantified by newly defined metrics that characterize the com plex ionic transport mechanism.Our results provide fundamental understanding of the physical origins of the reported variability in ionic conductivities based on an extensive analysis of literature data,while simultaneously outlining practical design guidance for achieving desired ionic transport properties based on conditions for which sensitivity to microstructural features is highest.Additional implications of our results are discussed,including a possible connection between ion conduction behavior and dendrite formation.展开更多
We propose an effective method for removing thermal vibrations that complicate the task of analyzing complex dynamics in atomistic simulation of condensed matter.Our method iteratively subtracts thermal noises or pert...We propose an effective method for removing thermal vibrations that complicate the task of analyzing complex dynamics in atomistic simulation of condensed matter.Our method iteratively subtracts thermal noises or perturbations in atomic positions using a denoising score function trained on synthetically noised but otherwise perfect crystal lattices.The resulting denoised structures clearly reveal underlying crystal order while retaining disorder associated with crystal defects.Purely geometric,agnostic to interatomic potentials,and trained without inputs from explicit simulations,our denoiser can be applied to simulation data generated from vastly different interatomic interactions.The denoiser is shown to improve existing classification methods,such as common neighbor analysis and polyhedral template matching,reaching perfect classification accuracy on a recent benchmark dataset of thermally perturbed structures up to the melting point.Demonstrated here in a wide variety of atomistic simulation contexts,the denoiser is general,robust,and readily extendable to delineate order from disorder in structurally and chemically complex materials.展开更多
基金The authors are partially supported by the Laboratory Directed Research and Development(LDRD)program(20-SI-004)at Lawrence Livermore National LaboratoryThis work was performed under the auspices of the US Department of Energy by Lawrence Livermore National Laboratory under contract No.DE-AC52-07NA27344.
文摘Graph neural networks are attractive for learning properties of atomic structures thanks to their intuitive graph encoding of atoms and bonds.However,conventional encoding does not include angular information,which is critical for describing atomic arrangements in disordered systems.In this work,we extend the recently proposed ALIGNN(Atomistic Line Graph Neural Network)encoding,which incorporates bond angles,to also include dihedral angles(ALIGNN-d).This simple extension leads to a memory-efficient graph representation that captures the complete geometry of atomic structures.ALIGNN-d is applied to predict the infrared optical response of dynamically disordered Cu(II)aqua complexes,leveraging the intrinsic interpretability to elucidate the relative contributions of individual structural components.Bond and dihedral angles are found to be critical contributors to the fine structure of the absorption response,with distortions that represent transitions between more common geometries exhibiting the strongest absorption intensity.Future directions for further development of ALIGNN-d are discussed.
基金This work was performed under the auspices of the U.S.Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344The authors acknowledge financial support from the U.S.Department of Energy(DOE),Office of Energy Efficiency and Renewable Energy,Vehicle Technologies Office,through the Battery Materials Research program.This work was partially funded by the Laboratory Directed Research and Development Program at LLNL under the project tracking code 15-ERD-022 and 18-FS-019.Additional computing support came from the LLNL Institutional Computing Grand Challenge program.The work of A.Grieder and N.Adelstein was supported by the National Science Foundation under Grant No.DMR-1710630 and simulations utilized the Extreme Science and Engineering Discovery Environment(XSEDE)83 Stampede2 at the University of Texas,Austin through allocation DMR180033.Work at The Pennsylvania State University is partially supported by the Donald W.Hamer Foundation through a Hamer Professorship.Helpful discussions about experimental microstructures of solid electrolytes with J.Ye(LLNL)are acknowledged.
文摘Although multiple oxide-based solid electrolyte materials with intrinsically high ionic conductivities have emerged,practical processing and synthesis routes introduce grain boundaries and other interfaces that can perturb primary conduction channels.To directly probe these effects,we demonstrate an efficient and general mesoscopic computational method capable of predicting effective ionic conductivity through a complex polycrystalline oxide-based solid electrolyte microstructure without relying on simplified equivalent circuit description.We parameterize the framework for Li_(7-x)La_(3)Zr_(2)0_(12)(LLZO)gamet solid electrolyte by combining synthetic microstructures from phase-field simulations with diffusivities from molecular dynamics simulations of ordered and disordered systems.Systematically designed simulations reveal an interdependence between atomistic and mesoscopic microstructural impacts on the effective ionic conductivity of polycrystalline LLZO,quantified by newly defined metrics that characterize the com plex ionic transport mechanism.Our results provide fundamental understanding of the physical origins of the reported variability in ionic conductivities based on an extensive analysis of literature data,while simultaneously outlining practical design guidance for achieving desired ionic transport properties based on conditions for which sensitivity to microstructural features is highest.Additional implications of our results are discussed,including a possible connection between ion conduction behavior and dendrite formation.
基金B.S.,C.W.P.,N.B.,and V.B.are partially supported by the Laboratory Directed Research and Development(LDRD)program(22-ERD-016)at Lawrence Livermore National LaboratoryThis work was performed under the auspices of the US Department of Energy by Lawrence Livermore National Laboratory under contract No.DE-AC52-07NA27344J.C.was partially supported by the Department of Mechanical Engineering’s startup grant at Boston University.
文摘We propose an effective method for removing thermal vibrations that complicate the task of analyzing complex dynamics in atomistic simulation of condensed matter.Our method iteratively subtracts thermal noises or perturbations in atomic positions using a denoising score function trained on synthetically noised but otherwise perfect crystal lattices.The resulting denoised structures clearly reveal underlying crystal order while retaining disorder associated with crystal defects.Purely geometric,agnostic to interatomic potentials,and trained without inputs from explicit simulations,our denoiser can be applied to simulation data generated from vastly different interatomic interactions.The denoiser is shown to improve existing classification methods,such as common neighbor analysis and polyhedral template matching,reaching perfect classification accuracy on a recent benchmark dataset of thermally perturbed structures up to the melting point.Demonstrated here in a wide variety of atomistic simulation contexts,the denoiser is general,robust,and readily extendable to delineate order from disorder in structurally and chemically complex materials.
