RbLi(NH<sub>2</sub>)<sub>2</sub>, a Rubidium Amido Lithiate Investigated by X‐Ray and Neutron Diffraction.

Jacobs, H.; Bohger, Patrick; Kockelmann, W.

doi:10.1002/chin.200244010

Cited by 1 publication

(1 citation statement)

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“…31 We discover three nitride-based fast ion conductor materials with quite complex compositions: Sr 2 LiCBr 3 N 2 , RbLi(H 2 N) 2 , and LiMgB 3 (H 9 N) 2 . Sr 2 LiCBr 3 N 2 is a solid-state carbodiimide material that was first characterized in 2005; 32 RbLi(H 2 N) 2 was originally synthesized in 1918 33 and subsequently characterized in 2002; 34 and LiMgB 3 (H 9 N) 2 was initially synthesized and studied in 2014 as a candidate solid-state hydrogen storage material. 35 The exotic stoichiometries of these materials underscores the utility of the ML-based model, as it seems unlikely that such complex materials would be discovered by chance to be fast ion conductors.…”

Section: Resultsmentioning

confidence: 99%

Machine Learning-Assisted Discovery of Solid Li-Ion Conducting Materials

et al. 2018

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We discover many new crystalline solid materials with fast single crystal Li ion conductivity at room temperature, discovered through density functional theory simulations guided by machine learning-based methods. The discovery of new solid Li superionic conductors is of critical importance to the development of safe all-solid-state Li-ion batteries. With a predictive universal structure-property relationship for fast ion conduction not well understood, the search for new solid Li ion conductors has relied largely on trial-and-error computational and experimental searches over the last several decades. In this work, we perform a guided search of materials space with a machine learning (ML)-based prediction model for material selection and density functional theory molecular dynamics (DFT-MD) simulations for calculating ionic conductivity.These materials are screened from over 12,000 experimentally synthesized and characterized candidates with very diverse structures and compositions. When compared to a random search of materials space, we find that the ML-guided search is 2.7 times more likely to identify fast Li ion conductors, with at least a 45x improvement in the log-average of room temperature Li ion conductivity. The F1 score of the ML-based model is 0.50, 3.5 times better than the F1 score expected from completely random guesswork. In a head-to-head competition against six Ph.D. students working in the field, we find that the ML-based model doubles the F1 score of human experts in its ability to identify fast Li-ion conductors from atomistic structure with a thousandfold increase in speed, clearly demonstrating the utility of this model for the research community. In addition to having high predicted Li-ion conductivity, all materials reported here lack transition metals to enhance stability against reduction by Li metal anode, and are predicted to exhibit low electronic conduction, high stability against oxidation, and high thermodynamic stability, making them promising candidates for solid-state electrolyte applications on these several essential metrics.

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