Redox flow batteries (RFBs) are a promising technology for stationary energy storage applications due to their flexible design, scalability, and low cost. In RFBs, energy is carried in flowable redox-active materials (redoxmers) which are stored externally and pumped to the cell during operation. Further improvements in the energy density of RFBs necessitates redoxmer designs with wider redox potential windows and higher solubility. Additionally, designing redoxmers with a fluorescence-enabled self-reporting functionality allows monitoring of the state of health of RFBs. To accelerate the discovery of redoxmers with desired properties, state-of-the-art machine learning (ML) methods, such as multiobjective Bayesian optimization (MBO), are useful. Here, we first employed density functional theory calculations to generate a database of reduction potentials, solvation free energies, and absorption wavelengths for 1400 redoxmer molecules based on a 2,1,3-benzothiadiazole (BzNSN) core structure. From the computed properties, we identified 22 Pareto-optimal molecules that represent best trade-off among all of the desired properties. We further utilized these data to develop and benchmark an MBO approach to identify candidates quickly and efficiently with multiple targeted properties. With MBO, optimal candidates from the 1400-molecule data set can be identified at least 15 times more efficiently compared to the brute force or random selection approach. Importantly, we utilized this approach for discovering promising redoxmers from an unseen database of 1 million BzNSN-based molecules, where we discovered 16 new Pareto-optimal molecules with significant improvements in properties over the initial 1400 molecules. We anticipate that this active learning technique is general and can be utilized for the discovery of any class of functional materials that satisfies multiple desired property criteria.

中文翻译：

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使用量子化学引导的多目标贝叶斯优化发现储能分子材料

氧化还原液流电池 (RFB) 因其灵活的设计、可扩展性和低成本而成为用于固定储能应用的有前途的技术。在 RFB 中，能量由可流动的氧化还原活性材料（氧化还原聚合物）携带，这些材料在外部储存并在运行期间泵送到电池中。RFB 能量密度的进一步改进需要具有更宽氧化还原电位窗口和更高溶解度的氧化还原聚合物设计。此外，设计具有启用荧光的自我报告功能的氧化还原聚合物可以监测 RFB 的健康状态。为了加速发现具有所需特性的氧化还原聚合物，最先进的机器学习 (ML) 方法，例如多目标贝叶斯优化 (MBO)，非常有用。这里，我们首先采用密度泛函理论计算来生成基于 2,1,3-苯并噻二唑 (BzNSN) 核心结构的 1400 个氧化还原分子的还原电位、溶剂化自由能和吸收波长的数据库。从计算的特性中，我们确定了 22 个帕累托最优分子，它们代表了所有所需特性之间的最佳权衡。我们进一步利用这些数据来开发 MBO 方法并对其进行基准测试，以通过多个目标属性快速有效地识别候选人。使用 MBO，与蛮力或随机选择方法相比，可从 1400 分子数据集中识别最佳候选者的效率至少提高 15 倍。重要的是，我们利用这种方法从 100 万个基于 BzNSN 的分子的看不见的数据库中发现了有希望的氧化还原聚体，在那里，我们发现了 16 个新的帕累托最优分子，与最初的 1400 个分子相比，它们的性质有了显着改进。我们预计这种主动学习技术是通用的，可用于发现满足多个所需属性标准的任何类别的功能材料。