Atomically dispersed amorphous catalysts: ab initio computational tools for a new frontier

该项目旨在开发和应用计算方法来了解非晶态载体材料上孤立的金属原子位的催化性能。计算结果将与菲利普斯石油乙烯聚合催化剂的实验数据进行比较，尽管长期以来一直存在关于活性中心和机理的问题，但菲利普斯石油乙烯聚合催化剂60年来一直是一种主要的工业催化剂。这些结果不仅将为菲利普斯催化剂的潜在改进提供特定的信息，还将改进理解广泛类别催化剂的理论工具，这些催化剂的活性由非晶态载体上的一小部分高活性金属中心主导。相关的教育和推广计划将提供给所有级别的学生，包括一个让高中生参与科学研究的游戏。这项研究将开发计算技术，以确定在无定形载体材料上的孤立金属中心中，使某些催化中心相对于其他催化中心具有高度活性的特定性质。这项工作主要集中在无定形二氧化硅(菲利普斯催化剂)上负载的铬，对此有大量的表征数据。计算方法将结合机器学习技术和稀有事件方法来分析站点的分布，以预测结构和活性之间的关系。具体地说，通过从头计算训练的机器学习方法将学习活性如何与二氧化硅表面孤立铬物种的局部结构环境有关。非玻尔兹曼采样技术将确保对具有异常高活性(低活化能)的稀有但重要的站点进行充分采样，以确保准确的站点平均动力学性质。这种结合的方法提供了一种新的“重要性学习”策略，可广泛用于建立活性中心分布的模型，识别高活性中心的关键特征，并在非晶态载体上设计出更好的原子分散催化剂。更广泛的教育和外联贡献包括支持重要性学习方法的“插件”的开发和公共共享、机器学习工具的虚拟现实可视化，以及为高中生设计的相关游戏，在该游戏中，他们将有机会与机器学习算法竞争，设计高度活跃的催化站点。

The project aims to develop and apply computational methods to understand the catalytic properties of isolated metal atom sites on amorphous support materials. The computational results will be compared to experimental data on the Phillips Petroleum ethylene polymerization catalyst which has been a workhorse industrial catalyst for 60 years despite longstanding questions about the active sites and the mechanism. The results will not only provide information specific to potential improvements in the Phillips catalyst, but will improve theoretical tools for understanding a broad class of catalysts where the activity is dominated by a small fraction of highly active metal sites on the amorphous support. Related educational and outreach programs will be offered to students at all levels, including a game to engage high school students in scientific pursuits.The study will develop computational techniques to identify the specific properties that make certain catalytic sites highly active relative to others among an ensemble of isolated metal sites on an amorphous support material. The work specifically focuses on chromium supported on amorphous silica (the Phillips catalyst) for which a broad body of characterization data is available. The computational approach will combine machine learning techniques and rare events methods for analyzing the distribution of sites to predict relationships between structure and activity. Specifically, machine learning methods trained by ab initio calculations will learn how activity is related to the local structural environments of the isolated chromium species on the silica surface. Non-Boltzmann sampling techniques will ensure that rare but important sites with unusually high activities (low activation energies) are adequately sampled to ensure accurate site-averaged kinetic properties. The combined approach provides a new "importance learning" strategy that can be broadly used to build models of active site distributions, identify critical characteristics of highly active sites, and engineer better atomically dispersed catalysts on amorphous supports. Broader educational and outreach contributions include the development and public sharing of "plug-ins" that support the importance learning approach, virtual reality visualization of the machine learning tools, and a related game for high school students in which they will have an opportunity to compete with the machine learning algorithm to design highly active catalytic sites.