Using Machine Learning and Data Mining to Leverage Community Knowledge for the Engineering of Stable Metal–Organic Frameworks

Although the tailored
metal active sites and porous architectures
of MOFs hold great promise for engineering challenges ranging from
gas separations to catalysis, a lack of understanding of how to improve
their stability limits their use in practice. To overcome this limitation,
we extract thousands of published reports of the key aspects of MOF
stability necessary for their practical application: the ability to
withstand high temperatures without degrading and the capacity to
be activated by removal of solvent molecules. From nearly 4000 manuscripts,
we use natural language processing and image analysis to obtain over
2000 solvent-removal stability measures and 3000 thermal degradation
temperatures. We analyze the relationships between stability properties
and the chemical and geometric structures in this set to identify
limits of prior heuristics derived from smaller sets of MOFs. By training
predictive machine learning (ML, i.e., Gaussian process and artificial
neural network) models to encode the structure–property relationships
with graph- and pore-structure-based representations, we are able
to make predictions of stability orders of magnitude faster than conventional
physics-based modeling or experiment. Interpretation of important
features in ML models provides insights that we use to identify strategies
to engineer increased stability into typically unstable 3d-transition-metal-containing
MOFs that are frequently targeted for catalytic applications. We expect
our approach to accelerate the time to discovery of stable, practical
MOF materials for a wide range of applications.

尽管金属有机框架（Metal-Organic Frameworks，MOFs）定制化的金属活性位点与多孔结构，在从气体分离到催化等诸多工程挑战中展现出巨大应用前景，但当前对如何提升其稳定性的认知不足，限制了其实际落地应用。为克服这一局限，我们从数千篇已发表学术文献中提取了MOF实际应用所需的稳定性关键属性：即耐高温降解的能力，以及通过脱除溶剂分子实现活化的性能。我们从近4000篇文献中，借助自然语言处理与图像分析技术，获取了超过2000条溶剂脱除稳定性数据与3000个热降解温度值。基于该数据集，我们分析了稳定性属性与化学、几何结构之间的关联，以此厘清此前基于小样本MOF数据集推导的经验法则的局限性。通过以基于图结构与孔道结构的表征方式编码结构-性能关联，训练预测型机器学习（Machine Learning，ML，即高斯过程与人工神经网络）模型，我们能够以比传统物理建模或实验快数个数量级的速度完成稳定性预测。对机器学习模型中关键特征的解读，为我们提供了设计思路：可针对催化应用中常被选为研究靶点的、通常不稳定的含3d过渡金属MOFs，通过结构工程手段提升其稳定性。我们期望本研究方法能够加速稳定实用型MOF材料的发现进程，以满足多领域的应用需求。