Francesco Ciucci1

1, HKUST, Kowloon, , Hong Kong

Fast ionic conductors are critically important for many applications ranging from fuel cells to batteries. For example, oxygen conduction and catalysis has been a key research focus in the field of highly efficient solid oxide fuel cells and solid electrolytes are key for high safety lithium batteries. We developed a data-driven framework to assist the analysis of ionic transport. Using unsupervised learning, we elucidated the diffusion characteristics in reference to the local atomic arrangement of various conductors including oxygen conductors, such as PrBaCo2O5.5 and La-doped BaFeO3 [1,2], and lithium conductors, i.e., lithium-rich anti-perovskites [3] and lithium-stuffed garnets [4].

The hopping analysis and local ionic configuration statistics explain the nature of vacancy-driven diffusivity in relation to the local atomic arrangement and composition. The presented works put forward a perspective on combining machine learning, statistics, and information theory to interpret results of molecular dynamics simulations.

[1] C Chen, ZM Baiyee, F Ciucci. Unraveling the effect of La A-site substitution on oxygen ion diffusion and oxygen catalysis in perovskite BaFeO3 by data-mining molecular dynamics and density functional theory. Physical Chemistry Chemical Physics 17 (37), 24011-24019 (2015)
[2] C Chen, D Chen, F Ciucci. A molecular dynamics study of oxygen ion diffusion in A-site ordered perovskite PrBaCo2O5.5: data mining the oxygen trajectories
Physical Chemistry Chemical Physics 17 (12), 7831-7837 (2015)
[3] Z Lu, C Chen, ZM Baiyee, X Chen, C Niu, F Ciucci. Defect chemistry and lithium transport in Li3OCl anti-perovskite superionic conductors. Physical Chemistry Chemical Physics 17 (48), 32547-32555 (2015)
[4] C Chen, Z Lu, F Ciucci. Data mining of molecular dynamics data reveals Li diffusion characteristics in garnet Li7La3Zr2O12. Scientific Reports 7, 40769 (2017)