Month: July 2021

Presentations

Electronic Structure 21 Workshop

Prof Ong gave a talk on “Addressing Errors in Ab Initio Molecular Dynamics Predictions through Machine Learning” at the Electronic Structure 21 Workshop held on Jul 14 2021. In this talk, we discuss the errors that AIMD simulations make in predicting the properties of lithium superionic conductors, which can be traced to the small simulation cell sizes, high temperatures and short simulation times. We then demonstrate how machine learning can be used to construct highly accurate interatomic potentials (ML-IAPs) to enable long time scale, large system simulations of complex materials. In particular, a standardized approach to construct ML-IAPs for lithium superionic conductors such as Li7P3S11, Li3YCl6 and Li0.33La0.56TiO3 (LLTO) is shown. All three superionic conductor solid electrolytes exhibit a transition between Arrhenius regimes are relatively low temperatures due to a change in diffusion dimensionality.

Publications

AIMD prediction errors in Lithium Superionic Conductors

Congratulations to Ji Qi on his paper on “Bridging the gap between simulated and experimental ionic conductivities in lithium superionic conductors” published in Materials Today Physics. Part of a “Special Issue on Solid state batteries: materials, characterizations, and understandings”, this is Ji’s first first-author paper. Lithium superionic conductors (LSCs) are of major importance as solid electrolytes for next-generation all-solid-state lithium-ion batteries. However, ab initio molecular dynamics (AIMD) often make wrong predictions of their ionic conductivities due to the short time scales and small cell sizes used. Here, we present a strategy to bridge this gap using machine learning interatomic potentials based on the moment tensor potential (MTP) formalism. We show that the DFT functional used to train the MTPs plays a critical role in the accuracy of the predictions. In particular, the van der Waals optB88 functional yield much more accurate lattice parameters, which in turn leads to accurate prediction of ionic conductivities and activation energies for Li0.33La0.56TiO3, Li3YCl6 and Li7P3S11. Nanosecond NPT MD simulations also reveal that all three lithium superionic conductors undergo a transition between two quasi-linear Arrhenius regimes at relatively low temperatures. This transition can be traced to an increase in the number and diversity of diffusion […]