CDS&E: Elucidating the Structure and Catalytic Activity of Nanoparticles Under Catalytic Conditions Using Ab Initio Machine Learning Force Fields

商业和消费化学产品的制造严重依赖催化反应过程，这些过程消耗了我国很大一部分能源资源，是温室气体排放的主要贡献者。这些催化剂通常由附着在不同材料载体上的金属催化剂纳米颗粒组成，可以呈现出几乎无限多的构型、形状、组成和工艺操作条件。纯粹通过实验来探索最优催化剂体系的巨大参数空间是不可能的，因此需要有效的模拟工具来探索原子水平上的催化剂行为。这项提案旨在开发这些模拟工具，重点是了解催化剂纳米颗粒形状在现实的反应器操作条件下是如何变化的；这将通过该提案的计划来实现，该计划使用先进的机器学习方法来提高分子动力学模拟的计算效率。计算模型将与已知的实验基准进行严格比较，以指导模拟器的开发并提高其预测精度。利用本研究开发的模拟工具发现的催化剂将有助于化学过程的脱碳，并将在发展循环化学经济中发挥重要作用。这项拟议的研究还将创造机会，教育下一代研究人员和行业领导者。本科生将学习编程技能，这将增加他们在新兴工作领域的竞争力，他们身临其境的研究经验将为他们在顶尖研究生院的职位和高等教育的职业生涯做好准备。使用该软件设计的催化剂的宏观效果图将通过3D打印生成，以向公众展示计算在加速催化剂设计中所起的作用。该提议旨在开发和应用从头算机器学习力场(AIMLFF)来模拟真实反应条件下的纳米粒子(NP)催化剂，并帮助阐明催化活性中心的性质。这项拟议的研究将具体解决这些挑战，方法是假设当AIMLFF根据周期密度泛函理论(DFT)计算的共同结构特征进行培训时，AIMLFF将能够在与反应相关的条件下直接对NP催化剂进行建模。这项工作将通过与文献中提供的基准质量显微镜和量热测量进行比较，解决与AIMLFF准确性有关的关键问题，并将加深对金属NPs的形状和可用结合位置如何依赖于金属物种和代表反应条件的温度和压力条件下的载体的一般理解。这项拟议的研究还将与高质量的X射线测量相比，更好地理解催化活性和NPs的演化之间的关系。对于对于当前模拟器来说太大的系统，我们将获得关于支撑金属NPs的平衡形状和缺陷密度如何随实际反应时间变化的基本知识。该奖项反映了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。

The manufacture of commercial and consumer chemical products relies heavily on catalytic reaction processes that consume a significant fraction of our nation's energy resources and are a major contributor to the emission of green-house gases. These catalysts often are composed of a metallic catalyst nanoparticle attached to a support of different material and can take on a nearly infinite number of configurations, shapes, compositions, and process operating conditions. To probe this vast parameter space for the optimal catalyst system purely by experimentation would be impossible and so efficient simulation tools are needed to explore catalyst behavior at the atomistic level. This proposal seeks to develop these simulation tools with emphasis on understanding how catalyst nanoparticle shapes change during realistic reactor operating conditions; this will be made possible by the proposal’s plan to improve the computational efficiency of molecular dynamics simulations using advanced machine learning methods. The computational models will be rigorously compared against known experimental benchmarks to guide simulator development and improve its prediction accuracy. The catalysts discovered using the simulation tools developed in this research program will contribute to the decarbonization of the chemical processes and will play an important role in developing circular chemical economies. The proposed research also will create opportunities to educate the next generation of researchers and industry leaders. Undergraduate students will learn programming skills that will increase their competitiveness in emerging job fields, and their immersive research experiences will prepare them for positions at top graduate schools and careers in higher education. Macroscopic renderings of catalysts designed with this software will be generated by 3D printing to demonstrate to the public the role computations play in accelerating catalyst design.This proposal seeks to develop and apply ab initio machine learning force fields (AIMLFF) to simulate nanoparticle (NP) catalysts under realistic reaction conditions and to help elucidate the nature of catalytic active sites. This proposed research will specifically address these challenges by hypothesizing that when AIMLFFs are trained on common structural features of periodic density functional theory (DFT) calculations that the community has identified as meaningful representations of NP catalysts, AIMLFF will be able to model NP catalysts directly under reaction-relevant conditions. This work will address critical questions related to the accuracy of the AIMLFFs by making comparisons to benchmark-quality microscopic and calorimetric measurements available in the literature, and will develop a general understanding of how the shape of metal NPs and available binding sites depend on the species of metal and the support under temperature and pressure conditions representative of reaction conditions. The proposed research will also develop an improved understanding of the relationship between catalytic activity and the evolution of NPs in comparison with high-quality X-ray measurements. Fundamental knowledge will be gained on how the equilibrium shape and defect density of supported metal NPs change over realistic reaction times for systems that are too large for current simulators.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.