Machine Learning Driven Optimization of Electrolyte for Highly Reversible Zn-Air Batteries with Superior Long-Term Cycling Performance.

Aqueous alkaline Zn-air batteries (ZABs) have garnered widespread attention due to their high energy density and safety, however, the poor electrochemical reversibility of Zn and low battery round-trip efficiency strongly limit their further development. The manipulation of an intricate microscopic balance among anode/electrolyte/cathode, to enhance the performance of ZABs, critically relies on the formula of electrolytes. Herein, the Bayesian optimization approach is employed to achieve the effective design of optimal compositions of multicomponent electrolytes, resulting in the remarkable enhancement of ZAB performance.

Bayesian Optimization-guided Discovery of High-performance Methane Combustion Catalysts based on Multi-component PtPd@CeZrOx Core-Shell Nanospheres.

Formula regulation of multi-component catalysts by manual search is undoubtedly a time-consuming task, which has severely impeded the development efficiency of high-performance catalysts. In this work, PtPd@CeZrO core-shell nanospheres, as a successful case study, is explicitly demonstrated how Bayesian optimization (BO) accelerates the discovery of methane combustion catalysts with the optimal formula ratio (the Pt/Pd mole ratio ranges from 1/2.33-1/9.

Inverse Design of Complex Block Copolymers for Exotic Self-Assembled Structures Based on Bayesian Optimization.

Variable chain topologies of multiblock copolymers provide great opportunities for the formation of numerous self-assembled nanostructures with promising potential applications. However, the consequent large parameter space poses new challenges for searching the stable parameter region of desired novel structures. In this Letter, by combining Bayesian optimization (BO), fast Fourier transform-assisted 3D convolutional neural network (FFT-3DCNN), and self-consistent field theory (SCFT), we develop a data-driven and fully automated inverse design framework to search for the desired novel structures self-assembled by ABC-type multiblock copolymers.