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

为了满足对具有成本效益和可持续的能量存储解决方案的日益增长的需求，有机氧化还原活性材料如醌和吩嗪衍生物在氧化还原液流电池中的使用已经获得了突出地位。然而，诸如溶解性、氧化还原反应稳定性和空气稳定性的挑战仍然存在。目前的合成方法仍然主要是基于试错法，因此计算高通量筛选策略提供了一种有价值的手段来发现新的有机氧化还原分子。在这项提案中，我们将结合联合收割机的计算和实验方法，以加速设计和合成的有机氧化还原分子的电池应用。我们将利用文献和合作者在Li/Na离子电池和有机氧化还原液流电池中的有机电极材料上产生的大量数据来建立分子库，并提供关键参数以建立结构-性能关系。计算建模、高通量筛选和机器学习将用于快速预测高潜力候选物，以便与合作者进行实验合成和测试。然后，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发现，并培养化学学生发展相关技能。

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.