A universal strategy of multi-objective active learning to accelerate the discovery of organic electrode molecules

Organic electrode molecules hold significant potential as the next generation of cathode materials for Li-ion batteries. In this study, we have introduced a multi-objective active learning framework that leverages Bayesian optimization and non-dominated sorting genetic algorithms-II. This framework enables the selection of organic molecules characterized by high theoretical energy density and low gap (LUMO-HOMO) (LUMO, lowest unoccupied molecular orbital; HOMO, highest occupied molecular orbital). Remarkably, after only two cycles of active learning, the determination of coefficient can reach 0.962 for theoretical energy density and 0.920 for the gap with a modest dataset of 300 molecules, showcasing superior predictive capabilities. The 2,3,5,6-tetrafluorocyclohexa-2,5-diene-1,4-dione, selected by non-dominated sorting genetic algorithms-II, has been successfully applied to Li-ion batteries as cathode materials, demonstrating a high capacity of 288 mAh g⁻¹ and a long cycle life of 1,000 cycles. This outcome underscores the high reliability of our framework. Furthermore, we have also validated the universality and transferability of our framework by applying it to two additional databases, the QM9 and OMEAD. When the training dataset of the model includes at least 500 molecules, the determination of coefficient essentially reaches approximately 0.900 for four targets: gap, reduction potential, LUMO, and HOMO. Therefore, the universal framework in our work provides innovative insights applicable to other domains to expedite the screening process for target materials.