Liu Tsung-Wei, Nguyen Quan, Dieng Adji Bousso, Gómez-Gualdrón Diego A
Department of Chemical and Biological Engineering, Colorado School of Mines 1601 Illinois St Golden CO 80401 USA
Department of Computer Science and Engineering, Washington University in St. Louis 1 Brookings Dr St. Louis MO 63130 USA.
Chem Sci. 2024 Oct 16;15(45):18903-19. doi: 10.1039/d4sc03609c.
Metal-organic frameworks (MOFs) promise to engender technology-enabling properties for numerous applications. However, one significant challenge in MOF development is their overwhelmingly large design space, which is intractable to fully explore even computationally. To find diverse optimal MOF designs without exploring the full design space, we develop Vendi Bayesian optimization (VBO), a new algorithm that combines traditional Bayesian optimization with the Vendi score, a recently introduced interpretable diversity measure. Both Bayesian optimization and the Vendi score require a kernel similarity function, we therefore also introduce a novel similarity function in the space of MOFs that accounts for both chemical and structural features. This new similarity metric enables VBO to find optimal MOFs with properties that may depend on both chemistry and structure. We statistically assessed VBO by its ability to optimize three NH-adsorption dependent performance metrics that depend, to different degrees, on MOF chemistry and structure. With ten simulated campaigns done for each metric, VBO consistently outperformed random search to find high-performing designs within a 1000-MOF subset for (i) NH storage, (ii) NH removal from membrane plasma reactors, and (iii) NH capture from air. Then, with one campaign dedicated to finding optimal MOFs for NH storage in a "hybrid" ∼10 000-MOF database, we identify twelve extant and eight hypothesized MOF designs with potentially record-breaking working capacity Δ between 300 K and 400 K at 1 bar. Specifically, the best MOF designs are predicted to (i) achieve Δ values between 23.6 and 29.3 mmol g, potentially surpassing those that MOFs previously experimentally tested for NH adsorption would have at the proposed operation conditions, (ii) be thermally stable at the operation conditions and (iii) require only 10% of the energy content in NH to release the stored molecule from the MOF. Finally, the analysis of the generated simulation data during the search indicates that a pore size of around 10 Å, a heat of adsorption around 33 kJ mol, and the presence of Ca could be part of MOF design rules that could help optimize NH working capacity at the proposed operation conditions.
金属有机框架材料（MOFs）有望为众多应用带来支持技术的特性。然而，MOF开发中的一个重大挑战是其庞大得令人难以应对的设计空间，即便通过计算也难以全面探索。为了在不探索整个设计空间的情况下找到多样的最优MOF设计，我们开发了Vendi贝叶斯优化算法（VBO），这是一种将传统贝叶斯优化与Vendi分数相结合的新算法，Vendi分数是最近引入的一种可解释的多样性度量。贝叶斯优化和Vendi分数都需要一个核相似性函数，因此我们还在MOF空间中引入了一种新颖的相似性函数，该函数兼顾了化学和结构特征。这种新的相似性度量使VBO能够找到具有可能同时依赖于化学和结构的性质的最优MOF。我们通过VBO优化三个与NH吸附相关的性能指标的能力对其进行了统计评估，这些指标在不同程度上依赖于MOF的化学和结构。针对每个指标进行了十次模拟实验，在一个包含1000种MOF的子集中，VBO在寻找高性能设计方面始终优于随机搜索，这些设计用于（i）NH存储、（ii）从膜等离子体反应器中去除NH以及（iii）从空气中捕获NH。然后，在一个“混合”的约10000种MOF数据库中进行了一次专门寻找用于NH存储的最优MOF的实验，我们确定了12种现存的和八种假设的MOF设计，它们在1巴压力下300 K至400 K之间可能具有破纪录的工作容量Δ 。具体而言，预测最佳的MOF设计将（i）实现23.6至29.3 mmol g之间的Δ 值，可能超过先前针对NH吸附进行实验测试的MOF在建议操作条件下所能达到的值，（ii）在操作条件下具有热稳定性，并且（iii）仅需NH中10%的能量含量即可从MOF中释放存储的分子。最后，对搜索过程中生成的模拟数据的分析表明，孔径约为10 Å、吸附热约为33 kJ mol以及存在Ca可能是MOF设计规则的一部分，这些规则有助于在建议的操作条件下优化NH工作容量。
