Computational design and high-throughput experimentation to discover organic redox-active molecules

为了满足日益增长的成本效益和可持续能源存储解决方案的需求，有机氧化还原活性材料，如醌和吩那嗪衍生物，在氧化还原液流电池中得到了突出的应用。然而，诸如溶解度、氧化还原反应稳定性和空气稳定性等挑战仍然存在。目前的合成方法仍然主要基于试错，因此计算高通量筛选策略为发现新的有机氧化还原分子提供了有价值的手段。在本提案中，我们将结合计算和实验方法来加速电池应用的有机氧化还原分子的设计和合成。我们将利用文献和合作者对Li/ na离子电池和有机氧化还原液流电池中有机电极材料产生的丰富数据来建立分子库，并完成建立结构-性质关系的关键参数。计算建模、高通量筛选和机器学习将被应用于快速预测高潜力的候选物质，以便与合作者进行实验合成和测试。生成的实验数据将用于进一步指导新分子的设计。该项目将通过计算、数据分析和高通量实验加速材料的发现，并培养化学专业学生的相关技能。

To meet the increasing demand of cost-effective and sustainable energy storage solutions, organic redox-active materials such as quinone and phenazine derivatives have gained prominence for use in redox flow batteries. However, challenges such as solubility, redox reaction stability and air stability remain. The current synthesis approach is still mainly based on trial and error and therefore computational high-throughput screening strategies offer a valuable means to discover novel organic redox molecules. In this proposal, we will combine computational and experimental approaches to accelerate the design and synthesis of organic redox molecules for battery applications. We will draw upon the wealth of data generated on organic electrode materials in Li/Na-ion batteries and organic redox flow batteries in the literature and from collaborators to build a library of molecules, complete with key parameters to establish structure-property relationships. Computational modelling, high-throughput screening and machine learning will be applied to rapidly predict high-potential candidates for experimental synthesis and testing with collaborators. The generated experimental data will be then used to further guide the design of novel molecules. This project will accelerate materials discovery via computation, data analysis and high-throughput experimentation and train the Chemistry student in developing relevant skills.