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

作　　者：Jiayi Du Jun Guo Wei Liu Ziwei Li Gang Huang Xinbo Zhang 

机构地区：[1]State Key Laboratory of Rare Earth Resource Utilization,Changchun Institute of Applied Chemistry,Chinese Academy of Sciences,Changchun 130022,China [2]School of Applied Chemistry and Engineering,University of Science and Technology of China,Hefei 230026,China [3]School of Materials Science and Engineering,Changchun University of Science and Technology,Changchun 130022,China

出　　处：《Science China Chemistry》2024年第11期3681-3687,共7页中国科学（化学英文版）

基　　金：supported by the National Key R&D Program of China (2022YFB2402200);the National Natural Science Foundation of China (92372206, 52271140, 52171194);the Jilin Province Science and Technology Development Plan Funding Project (YDZJ202301ZYTS545);the National Natural Science Foundation of China Excellent Young Scientists (Overseas);the Youth Innovation Promotion Association CAS (2020230)。

摘　　要：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 m Ah 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 approximately0.900 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.

关 键 词：organic electrode molecules Li-ion batteries active learning multi-objective Bayesian optimization 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] O621[自动化与计算机技术—控制科学与工程] TM912[理学—有机化学]