Study of Li atom diffusion in amorphous Li3PO4 with neural network potential

Abstract

To clarify atomic diffusion in amorphous materials, which is important in novel information and energy devices, theoretical methods having both reliability and computational speed are eagerly anticipated. In the present study, we applied neural network (NN) potentials, a recently developed machine learning technique, to the study of atom diffusion in amorphous materials, using LiPO as a benchmark material. The NN potential was used together with the nudged elastic band, kinetic Monte Carlo, and molecular dynamics methods to characterize Li vacancy diffusion behavior in the amorphous LiPO model. By comparing these results with corresponding DFT calculations, we found that the average error of the NN potential is 0.048 eV in calculating energy barriers of diffusion paths, and 0.041 eV in diffusion activation energy. Moreover, the diffusion coefficients obtained from molecular dynamics are always consistent with those from ab initio molecular dynamics simulation, while the computation speed of the NN potential is 3-4 orders of magnitude faster than DFT. Lastly, the structure of amorphous LiPO and the ion transport properties in it were studied with the NN potential using a large supercell model containing more than 1000 atoms. The formation of PO units was observed, which is consistent with the experimental characterization. The Li diffusion activation energy was estimated to be 0.55 eV, which agrees well with the experimental measurements.

A Critical Review of Machine Learning of Energy Materials

“…a) The Arrhenius plot of Li diffusion coefficients obtained from KMC and MD simulations with NNP and DFT, respectively. b) The Arrhenius plot of Li diffusion coefficients in amorphous Li 3 PO 4 from large‐scale MD simulations . c) Schematic workflow of sampling strategy combining GA and specialized NNP.…”

“…d) The predicted Haven ratio and e) the Arrhenius plot of Li diffusion diffusivity in α‐Li 3 N from eSNAP MD simulations . Reproduced with permission . Copyright 2017 American Institute of Physics and 2018 American Institute of Physics.…”

“…In addition to the studies on Li compounds, the modeling of dynamical intercalation of Li atoms into the electrodes (i.e., guest atoms in host frameworks) is also of great interest. Fujikake et al [221] recently have introduced a newly developed GAP model by fitting the energy and force differences induced [216] c) Schematic workflow of sampling strategy combining GA and specialized NNP. At each delithiation step, GA was used to identify the most stable Li/vacancy arrangement of that composition, with specialized NNP determining the energetics of different arrangements.…”

“…[131] Reproduced with permission. [216,217] Copyright 2017 American Institute of Physics and 2018 American Institute of Physics. d,e) Reprinted under the terms of the CC-BY license.…”

Machine learning for the modeling of interfaces in energy storage and conversion materials

“…Amorphous lithium phosphate (Li 3 PO 4 ) is a Li ion conductor with potential applications as solid electrolyte in all-solid batteries [100]. Li, Watanabe et al employed ANN potentials trained on DFT data to investigate Li transport in amorphous Li 3 PO 4 [95]. The study considered both small DFT-generated structure models of stoichiometric Li 3 PO 4 and large models (up to ∼1000 atoms) with slightly Li-deficient compositions directly from ANN-potential calculations (figure 8), all of which were obtained from melt-quench simulations.…”