Collaborative Research: Atomic Level Structural Dynamics in Catalysts

催化剂有助于加快化学反应，其发展影响了能源，环境，生物技术和药物设计等领域。该项目的愿景是利用现代统计学和机器学习的计算工具来执行数据驱动的新催化剂发现。为此，一个合作团队与催化剂，材料科学，生物物理学，计算建模，统计学，信号处理和数据科学的互补专业知识组装。如何加速反应取决于催化剂及其相关化学反应物（催化体系）的结构和形状的动态变化。该项目的目标是在原子水平上探索，描述和量化酶和纳米颗粒催化剂的动态结构。显微镜和光谱学的最新进展现在使人们能够非常详细地测量时间和空间维度的动态变化。该项目将数据科学的最新进展与这些新的实验工具相结合，以提取描述催化系统动态行为的特征。此外，该项目将加强数据密集型和跨学科科学教育基础设施的发展，促进劳动力发展，促进科学领域的性别平等，并传播科学知识。本研究的指导假设是，如果不描述反应物与催化剂的分子相互作用所引发的原子级结构变化，就不能完全理解催化功能。利用电子显微镜和单分子荧光共振能量转移光谱获得的实验数据集来测试这一假设，以探索纳米颗粒和酶的结构动力学。数据分析工作流程，它集成了去噪，降维，聚类和动态马尔可夫模型，使时空分辨测量的复杂动态演变的描述和分类。该研究开发并应用先进的方法来处理嘈杂的高维数据-动态系统分析的关键瓶颈。从实验数据中提取的信息指导统计物理框架内的蛋白质和纳米粒子的构象空间的计算采样，使用超级计算机技术。这些信息有助于物理模型的发展，探测目前实验无法达到的现象，如皮秒核运动，以及蛋白质构象变化及其与化学事件的耦合。变革性的影响是通过建立动态系统响应和催化功能之间的联系来更好地理解催化。通过该项目开发的计算方法有可能普遍应用于材料科学和结构生物学中的许多基础问题，其中动态行为很重要。该项目是美国国家科学基金会利用数据革命（HDR）大创意活动的一部分，该奖项由人类发展研究所和NSF数学与物理科学理事会化学部共同支持。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。

Catalysts help make chemical reactions go faster and their development impact areas such as energy, the environment, biotechnology, and drug design. The vision of this project is to harness computational tools from modern statistics and machine learning to perform data-driven discovery of new catalysts. To this end, a collaborative team is assembled with the complementary expertise in catalysts, materials science, biophysics, computational modelling, statistics, signal processing, and data science. How a reaction is accelerated depends on the dynamic changes in the structure and shape of a catalyst and its associated chemical reactants (a catalytic system). The goal of this project is to explore, describe, and quantify the dynamic structures of enzyme and nanoparticle catalysts at the atomic level. Recent advances in microscopy and spectroscopy now make it possible to measure with great detail dynamic changes in time and in dimensional space. This project combines recent advances in data science with these new experimental tools to extract features that describe the dynamic behaviour of catalytic systems. In addition, the project will enhance the development of educational infrastructure for data-intensive and interdisciplinary science, contribute to workforce development, promote gender equality in the sciences, and disseminate scientific knowledge. The guiding hypothesis of this research is that catalytic functionality cannot be fully understood without describing the atomic-level structural changes triggered by the molecular interactions of reactants with the catalyst. This hypothesis is tested by utilizing experimental datasets obtained from electron microscopy and single-molecule fluorescence resonance energy-transfer spectroscopy to explore structural dynamics in nanoparticles and enzymes. A data-analysis workflow, which integrates denoising, dimensionality reduction, clustering, and dynamic Markovian modelling, enables descriptions and classifications of the complex dynamical evolutions in spatiotemporally resolved measurements. The research develops and applies advanced methodologies to process noisy, high-dimensional data - a crucial bottleneck for the analysis of dynamic systems. The information extracted from experimental data guides the computational sampling of the conformational space of proteins and nanoparticles within a statistical physics framework, using supercomputer technology. This information facilitates the development of physical models that probe phenomena that are currently experimentally inaccessible, such as picosecond nuclear motions, as well as protein conformational changes and their coupling with chemical events. The transformative impact is to better understand catalysis by establishing a link between dynamic system response and catalytic functionality. The computational approaches developed through this project have the potential to be generally applied to many fundamental problems in materials science and structural biology where dynamic behaviours are important.This project is part of the National Science Foundation's Harnessing the Data Revolution (HDR) Big Idea activity, and is jointly supported by the HDR and the Division of Chemistry within the NSF Directorate of Mathematical and Physical Sciences.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.