Machine Learning for the Discovery of Metal-Organic Frameworks for Hydrogen Storage Applications

EPSRC：Paula Nkulikiyinka：EP/R513027/1金属有机骨架(MOF)是一类具有超高孔隙率和高比表面积的晶体材料。MOF具有这些性质，以及其结构中有机和无机成分的可变性，在清洁能源中具有潜在的应用价值，最重要的是作为氢气和甲烷等气体的存储介质，以及满足各种分离需求的高容量吸附剂。定量结构-性质/活性关系(QSPR--机器学习的一种形式)的使用是一种新兴的有用的数学工具，它允许物理或化学性质之间的联系来预测分子的行为或所需的特征。这项研究的目的是利用这两个领域，以获得最优的MOF储氢，以及调查哪些参数影响吸氢能力。在筛选出最佳MOF之后，它们将在实验室合成以测试性能和验证方法。这项研究将由一名博士生(Paula Nkulikiyinka，由EPSRC DTP基金资助)与加拿大康科迪亚大学A Howarth博士的研究小组合作进行。克劳夫博士和豪沃斯博士都讨论并同意了这项研究提案。

EPSRC : Paula Nkulikiyinka : EP/R513027/1Metal-organic frameworks (MOFs) are a class of crystalline materials with ultrahigh porosity and high surface areas. With these properties, along with variability for both the organic and inorganic components of their structures, MOFs are of interest for potential applications in clean energy, most significantly as storage media for gases such as hydrogen and methane, and as high-capacity adsorbents to meet various separation needs. The use of quantitative structure-property/activity relationships (QSPRs - a form of machine learning) is an emerging and helpful mathematical tool that allows the link between physical or chemical properties to predict the behaviour or desired characteristic of a molecule. The purpose of this study is to exploit both fields in order to obtain the optimal MOF for hydrogen storage, as well as investigating what parameters affect the hydrogen uptake capabilities. Following the screening of optimal MOFs, they will be synthesized in the lab to test performance and validate the methodology.This research will be conducted by a PhD student (Paula Nkulikiyinka, who is funded by an EPSRC DTP grant) in collaboration with Dr A Howarth's research group at Concordia University, Canada. The research proposal has been discussed and agreed by both Dr Clough and Dr Howarth.