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
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.
