Li-Ion Conductive Li3PO4-Li3BO3-Li2SO4 Mixture: Prevision through Density Functional Molecular Dynamics and Machine Learning

Abstract

The development of high Li-ion conductive solid electrolytes is crucial for the practical use of all solid-state Li-ion batteries. The mixing of hetero Li-ion conductive substances is a known method for enhancing the Li-ion conductivity more than in the original substances. In this study, using computer simulations, we proved that a ternary Li3PO4-Li3BO3-Li2SO4 system has the potential to demonstrate improved Li-ion conductivity based on the introduction of a quasi-Li/oxygen vacancy. The Li-ion conductivities of this ternary system were calculated using several model systems based on the density functional molecular dynamics under an isothermal-isobaric ensemble. However, an exploration using the density functional molecular dynamics cannot cover the entire combinatorial space owing to a lack of computational capability. To search through a vast combinatorial space, we conducted analyses using a machine learning technique. The analysis results clarify the relationship between Li-ion conductivity and phonon free energy, and allow the optimum composition ratio with the highest Li-ion conductivity to be predicted.

Optimization of a Heterogeneous Ternary Li3PO4–Li3BO3–Li2SO4 Mixture for Li-Ion Conductivity by Machine Learning

“…This result, obtained using appropriate synthesis conditions for the individual samples, differs from the theoretical result obtained by assuming limited conditions on the crystal structure of composition due to the available computational load . Therefore, the hypothesis suggested in ref might be only one of the factors that elevates Li-ion conductivities in a mixed system.…”

“…This mixed material is stable under atmospheric conditions and can be sintered integrally with the positive and negative electrode materials using ceramic inductor manufacturing technology. In the simulation-based study, we predicted that the theoretical Li 3 PO 4 –Li 3 BO 3 –Li 2 SO 4 ternary system would be more conductive than the parent materials because the parent materials possess polyanions whose ionic valence are different each other . However, in our theoretical prediction, it was difficult to identify the optimum composition due to computational limitations.…”

“…In the simulation-based study, we predicted that the theoretical Li 3 PO 4 −Li 3 BO 3 −Li 2 SO 4 ternary system would be more conductive than the parent materials because the parent materials possess polyanions whose ionic valence are different each other. 24 However, in our theoretical prediction, it was difficult to identify the optimum composition due to computational limitations. This is the first study of ternary Li 3 PO 4 −Li 3 BO 3 −Li 2 SO 4 , though its binary parts have already been studied.…”

De Novo Creation of a Naked-Eye-Detectable Fluorescent Molecule Based on Quantum-Chemical Computation and Machine Learning

“…However, its work solely focuses on the analysis and speculation of the experimental results; the creative work such as the prediction of various phenomena and designing materials 25,[34][35][36][37][38] has rarely been considered. The recent applications of ML algorithms to chemistry and materials science represent a good turning point for computer-aided chemistry and materials science.…”

Bayesian-optimization-guided experimental search of NASICON-type solid electrolytes for all-solid-state Li-ion batteries

“…A total of 49 compositions, with the Ca and Y ratios divided at equal intervals, were synthesized, and their Li-ion conductivities evaluated. In addition, as a proof-ofconcept study the suitability of the Bayesian optimization (BO) approach [33][34][35][36][37] to efficiently explore the optimal composition was demonstrated. BO allows more efficient sampling than the conventional random sampling approach and enables the accelerated determination of the optimized composition.…”