Rapid Materials Discovery by Automated Machine Learning

通过自动化机器学习快速发现材料

• 批准号: 1852245
• 金额: --
• 依托单位: University of Manchester
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-1852245
• 关键词: Rapid; Materials; Discovery; Automated; Machine

Porous materials have a wide spectrum of important industrial applications. Recent developments in materials chemistry have shown that metal-organic frameworks (MOFs) have promising properties that complement or compete favourably with zeolites and activated carbons in various applications. MOFs are crystalline porous coordination polymers consisting of polyatomic organic ligands linked to metal ions/clusters by covalent bonds. MOFs have shown great promise for gas adsorption and storage owing to their high porosity and internal surface area, and tuneable functionality on the pore surface for selective gas binding. Generation of open metal sites and incorporation of pendant functional groups at the pore surface are two dominant methods of functionalising MOF cavities. Given an unlimited combination of metal ions and organic ligands, MOFs have an ultra-high degree of structure design flexibility and tuneability; over 6000 new MOFs in the past 10 years have been added to the CCDC database. We have recently discovered a family of very stable MOF materials (MFM-300 series) incorporating isophthalate linkers. MFM-300(Al) exhibits excellent performance in selective carbon capture and hydrocarbon separations. More recently, we reported the first example of studies on guest binding in a pair of isostructural redox-active MOFs. However, much of this discovery was empirical. The best route to predictive chemistry is via the automated (machine) learning of paired physical property-function relationships from large datasets. This PhD project will establish a new approach to the development of new MOF materials via state of the art Predictive Deep (machine) Learning (the technology behind Apple's Siri and Google driverless cars), encoded in KNIME (e.g. O'Hagan S, Kell DB: The KNIME workflow environment and its applications in Genetic Programming and machine learning. Genetic Progr Evol Mach 2015; 16:387-391). Predictive models suggest the materials to build and test, iteratively, and allow one to navigate the eeffective 'landscape' intelligently (see Currin et al. Chem Soc Rev 2015; 44:1172-1239). This mixed wet/dry project combines the expertise of Yang (on MOF synthesis and characterisation) and Kell (on Design of Experiments and machine learning) and is thus inter-disciplinary and transformative. We anticipate that a large family of new MOFs will be prepared at the end of this project, showing various exciting materials properties.

多孔材料具有广泛的重要工业应用。材料化学的最新发展表明，金属有机框架（MOFs）具有有前途的性能，在各种应用中与沸石和活性炭互补或竞争。MOF是由通过共价键连接到金属离子/簇的多原子有机配体组成的结晶多孔配位聚合物。由于其高孔隙率和内表面积以及用于选择性气体结合的孔表面上的可调节功能性，MOFs已经显示出用于气体吸附和储存的巨大前景。开放金属位点的产生和在孔表面处的侧基官能团的并入是官能化MOF腔的两种主要方法。由于金属离子和有机配体的无限组合，MOFs具有超高的结构设计灵活性和可调性;在过去的10年中，已有超过6000个新的MOFs被添加到CCDC数据库中。我们最近发现了一系列包含间苯二甲酸酯连接剂的非常稳定的MOF材料（MFM-300系列）。MFM-300（Al）在选择性碳捕集和烃类分离方面表现出优异的性能。最近，我们报道了第一个例子的研究客体结合在一对同构氧化还原活性的MOFs。然而，这一发现大部分是经验性的。 预测化学的最佳途径是通过从大型数据集中自动（机器）学习成对的物理性质-功能关系。这个博士项目将通过最先进的预测深度（机器）学习（Apple Siri和Google无人驾驶汽车背后的技术），以KNIME编码（例如O 'Hagan S，Kell DB：KNIME工作流环境及其在遗传编程和机器学习中的应用），建立开发新MOF材料的新方法。Genetic Progr Evol Mach 2015; 16：387-391）。预测模型建议迭代地构建和测试材料，并允许智能地导航有效的“景观”（参见Currin等人Chem Soc Rev 2015; 44：1172-1239）。这个混合的湿/干项目结合了Yang（关于MOF合成和表征）和Kell（关于实验设计和机器学习）的专业知识，因此是跨学科和变革性的。我们预计，在该项目结束时将制备一大系列新的MOF，显示各种令人兴奋的材料特性。