Materials space of solid-state electrolytes: unraveling chemical composition–structure–ionic conductivity relationships in garnet-type metal oxides using cheminformatics virtual screening approaches

Abstract

The organic electrolytes of most current commercial rechargeable Li-ion batteries (LiBs) are flammable, toxic, and have limited electrochemical energy windows. All-solid-state battery technology promises improved safety, cycling performance, electrochemical stability, and possibility of device miniaturization and enables a number of breakthrough technologies towards the development of new high power and energy density microbatteries for electronics with low processing cost, solid oxide fuel cells, electrochromic devices, etc. Currently, rational materials design is attracting significant attention, which has resulted in a strong demand for methodologies that can accelerate the design of materials with tailored properties; cheminformatics can be considered as an efficient tool in this respect. This study was focused on several aspects: (i) identification of the parameters responsible for high Li-ion conductivity in garnet structured oxides; (ii) development of quantitative models to elucidate composition-structure-Li ionic conductivity relationships, taking into account the experimental details of sample preparation; (iii) circumscription of the materials space of solid garnet-type electrolytes, which is attractive for virtual screening. Several candidate compounds have been recommended for synthesis as potential solid state electrolyte materials.

A Critical Review of Machine Learning of Energy Materials

“…In unsupervised learning, the goal is to identify patterns from data without input labels. In materials science, it has been applied to study the collective diffusion of ions and visualize complex high‐dimensional data . Lastly, reinforcement learning mimics how humans learn by interacting with environments; the algorithm improves in its ability to perform certain tasks through feedback in the form of rewards or punishments.…”

An investigation of the structural properties of Li and Na fast ion conductors using high-throughput bond-valence calculations and machine learning

“…In this regard, high-throughput screening of materials databases using first-principles simulation approaches has a demonstrated successful track record of guiding advances in materials science [see Jain et al (2016) for a recent review], including areas as diverse as heterogeneous catalysis (Greeley et al, 2002), bulk crystal structure prediction (Curtarolo et al, 2003;Meredig & Wolverton, 2010) and thermoelectricity (Carrete et al, 2014). In the domain of batteries, first-principles simulations based on density functional theory (DFT) have proven to be a useful method to understand the mechanisms of electroactive materials (Meng & Dompablo, 2009;Ceder, 2010;Islam & Fisher, 2014) and, in combination with high-throughput searches, are widely viewed as a promising approach to proposing new materials (Hautier et al, 2011;Cheng et al, 2015;Qu et al, 2015;Kirklin et al, 2013;Schü tter et al, 2015;Borodin et al, 2015;Kireeva & Pervov, 2017).…”

Materials Informatics Screening of Li‐Rich Layered Oxide Cathode Materials with Enhanced Characteristics Using Synthesis Data

“…Albeit this assumption can be seen as unreasonable it allowed us to significantly enhance the results of modeling. The descriptors describing the synthesis process are efficiently used in virtual screening applications in materials science . Heat‐treatment information including the temperature and time required for the calcination and sintering processes has been normalized to a scale of zero to one accepting the maximal and minimal known temperatures and time limits.…”