Tackling Ion Transport and Interfacial Evolutions in Solid-State Batteries through “Intelligent” Machine-Learning

Computational material science is crucial to establishing a firm link between complex phenomena occurring at the atomic scale and macroscopic observations of functional materials, such as energy materials for solar cells, fuel cells, and rechargeable batteries. Storing and distributing green energy is central to the modernization of our society. Rechargeable batteries, including lithium (Li)-ion batteries, contribute substantially to shifting away from oil and other petrochemicals. The 2019 Nobel Prize in Chemistry awarded to John Goodenough, Stanley Whittingham, and Akira Yoshino resulted in the Li-ion battery as a mainstream technology powering millions of portable devices, electric vehicles, and stationary applications.<br/>Commercial Li-ion batteries suffer from stability issues. All-solid-state batteries utilizing solid-electrolyte “membranes” separating the distinct chemistries of the electrode materials appear to be a safer alternative. Nevertheless, stabilizing solid-solid “buried” interfaces in all-solid-state batteries remains a poorly understood aspect. In my talk, I will showcase the power of simulations to inform the complex reaction mechanisms, which take place at these complex interfaces. My talk will address two main aspects: 1) The advancement of first-principles kinetic Monte Carlo to study transport in fast-ion conductors. 2) I will showcase how machine-learned potentials can bring insight into the metal-anode/sulfide electrolyte interfaces, such as that of Li-metal/Li₆PS₅Cl.