Special Seminar

Computational material science is crucial to establish 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 introduction of the Li-ion battery as a mainstream technology powering millions of portable devices, electric vehicles, and stationary applications.

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 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 machine- learning-driven simulations to inform the complex reaction mechanisms, which take place at these complex interfaces.

Furthermore, finding alternatives to the Li-ion battery appears a priority in diversifying and modernizing current energy storage technologies. When the life-cycle analysis is examined in the design of batteries, sodium (Na) is attractive because it can be "harvested" directly from seawater, making Na ~50 times lower in cost than Li. An important class of phosphate electrodes and electrolytes discovered by Hong and Goodenough is the Natrium Super lonic CONductors (NaSICONs) with chemical formula NaxM2(XO4)3, where M is transition metal, and X = Si, P and/or S. NaSICON electrode and electrolyte materials display significant Na-ion mobility. In this talk, I will demonstrate that first-principles methods, can guide the design of better NaSICON electrodes and electrolytes, with superior energy densities and improved ion transport. For example, our predictions indicate that suitably doped NASICON compositions, especially with high silicon content, can achieve high Na* mobilities. These findings push the optimization of mixed polyanion solid electrolytes and electrodes, including sulfide-based polyanion frameworks, which are known for their superior ionic conductivities.