Deep Potential generation scheme and simulation protocol for the Li10GeP2S12-type superionic conductors

Huang, Jianxing; Zhang, Linfeng; Wang, Han; Zhao, Jinbao; Cheng, Jun; E, Weinan

doi:10.48550/arxiv.2006.03320

Search citation statements

Order By: Relevance

Paper Sections

Select...

Introduction1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2021

Publication Types

Select...

Other1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

(1 citation statement)

References 42 publications

(43 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In recent years, machine learning (ML) PES as a function of local environment descriptors has emerged as an especially promising, and reproducible approach to develop IAPs with near-DFT accuracy in energies and forces. [38][39][40][41][42][43][44][45][46][47][48][49][50] However, most ML-IAPs that have been developed in the literature still rely on DFT calculations performed using the PBE functional; as such, their performance are still limited by the accuracy of the DFT training data.…”

Section: Introductionmentioning

confidence: 99%

Bridging the Gap Between Simulated and Experimental Ionic Conductivities in Lithium Superionic Conductors

Qi¹,

Banerjee²,

Zuo³

et al. 2021

Preprint

View full text Add to dashboard Cite

Lithium superionic conductors are of major importance as solid electrolytes for next-generation all-solid-state lithium-ion batteries. While ab initio molecular dynamics have been extensively applied to study these materials, there are often large discrepancies between predicted and experimentally measured ionic conductivities and activation energies due to the high temperatures and short time scales of such simulations. Here, we present a strategy to bridge this gap using moment tensor potentials (MTPs). We show that MTPs trained on energies and forces computed using 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 the Li 0.

show abstract