CAREER: Introducing synthetic feasibility predictions in large scale computational screening of open-framework materials (OFMs)

金属有机骨架(MOF)是一种具有可调纳米结构的材料，它有可能给包括催化、储能和化学分离在内的一系列技术带来革命性的变化。拟议的研究项目旨在利用包括高通量计算筛选和机器学习工具在内的先进计算方法，加快发现具有定制性质的可合成MOF。拟议的教育和外联计划旨在为STEM中代表性不足的群体的学生提供研究实习机会，并通过基于网络的互动活动吸引更多的受众。该研究项目将提供关于MOF形成机制的基本知识和新的理解，这将使实验者能够探索更广泛的潜在MOF参数空间。这可能会对MOF合成的重要领域及其众多应用产生变革性的影响。MOF的可合成性将通过估计(I)晶格能、(Ii)MOF自由能、(Iii)溶剂负载的MOF自由能和/或(Iv)关键自组装步骤的自由势垒来确定。所获得的知识将被用来提供MOF综合因子，该因子将使用机器学习工具在大型数据库中高效地计算。提议的方法将创建MOF数据库，为每个假设的MOF提供综合可行性。这些预测的验证将通过与实验者的合作进行。拟议的研究和教育整合将通过一个针对高中生和本科生的研究体验计划，针对STEM中代表性不足的群体。从财政部数据库中获得的知识将被纳入教育电影。将开发一个允许进行虚拟化学实验的互动教学网站，并向普通观众开放。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Metal-organic frameworks (MOFs) are materials with tunable nanoscale structures that have the potential to revolutionize a wide spectrum of technologies, including catalysis, energy storage, and chemical separations. The proposed research project aims to accelerate the discovery of synthesizable MOFs with tailored properties using advanced computational methods including high-throughput computational screening and machine learning tools. The proposed educational and outreach plan aims to develop research internships for students from underrepresented groups in STEM and to engage a broader audience via interactive web-based activities. The research project would provide fundamental knowledge and new understanding of MOF formation mechanisms which would allow experimentalists to explore a much broader range of the vast parameter space of potential MOFs. This could have a transformational impact on the important field of MOF synthesis and its numerous applications. MOF synthesizability will be determined by estimating (i) lattice energy, (ii) MOF free energy, (iii) solvent-loaded MOF free energy, and/or (iv) free energy barriers for key self-assembly steps. The gained knowledge will be used to provide a MOF synthesizability factor, which will be calculate efficiently for large databases using machine learning tools. The proposed approach will create MOF databases that provide synthetic feasibility for each hypothesize MOF. Validation of the predictions will be performed through collaborative work with experimentalists. The proposed integration of research and education will target underrepresented groups in STEM through a research experience program for high school and undergraduate students. The knowledge obtained from MOF databases will be incorporated into educational movies. An interactive pedagogical website that allows performing virtual chemistry experiments will be developed and made accessible to general audiences.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.