Three-dimensional microbattery design via an automatic geometry generator and machine-learning-based performance simulator

Three-dimensional (3D) microbatteries are promising candidates as power sources for internet-of-things (IoT) devices. To accelerate the design of 3D microbatteries, we develop and apply a battery-optimization system that consists of an automatic geometry generator coupled with an efficient machine-learning (ML) platform. The input into the analysis is a small amount of performance data from continuum simulations. Because ML-based simulators evaluate the battery performance quickly at an accuracy comparable with that of continuum simulations, it is possible to discover high-performance batteries from many candidate geometries. We successfully design a set of microbatteries having greater energy than the simplest interdigitated-plate configuration, without reducing the power. The results show that the optimal geometry changes with the applied current. One geometry displays 1.4 times greater energy at 3.16 mA/cm² and the other shows 6.5 times greater energy at 15.8 mA/cm² compared with the reference geometry. The results exemplify the benefit of our method.