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

金属有机框架（MOFs）是具有可调纳米结构的材料，有可能彻底改变广泛的技术，包括催化，储能和化学分离。拟议的研究项目旨在使用先进的计算方法，包括高通量计算筛选和机器学习工具，加速发现具有定制特性的可合成MOFs。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开发研究实习机会，并通过互动式网络活动吸引更广泛的受众。该研究项目将提供对MOF形成机制的基础知识和新的理解，这将使实验人员能够探索潜在MOF的更广泛的参数空间。这可能对MOF合成及其众多应用的重要领域产生变革性影响。通过估计（i）晶格能、（ii）MOF自由能、（iii）负载溶剂的MOF自由能和/或（iv）关键自组装步骤的自由能垒来确定MOF可合成性。获得的知识将用于提供MOF可合成性因子，该因子将使用机器学习工具针对大型数据库进行有效计算。所提出的方法将创建MOF数据库，为每个假设的MOF提供合成可行性。预测的验证将通过与实验人员的合作进行。拟议的研究和教育的整合将通过高中和本科生的研究经验计划，针对STEM中代表性不足的群体。从财政部数据库获得的知识将被纳入教育电影。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值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.