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您的检索式：作者名="Linus C.Erhard"
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| 1 | A machine-learned interatomic potential for silica and its relation to empirical models显示文摘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. | Linus C.Erhard Jochen Rohrer Karsten Albe Volker L.Deringer | 2022 | npj Computational Materials2022，，1： | 3 |
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