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

CDS

基本信息

批准号：
2245120
负责人：
Tibor Szilvasi
金额：
$ 29.13万
依托单位：
University of Alabama Tuscaloosa
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2023
资助国家：
美国
起止时间：
2023-08-15 至 2026-07-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2245120&HistoricalAwards=false
关键词：
CDS&amp Elucidating Structure Catalytic Activity

项目摘要

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.

商业和消费化学产品的制造严重依赖于催化反应过程，这些过程消耗了我国能源的很大一部分，并且是温室气体排放的主要贡献者。这些催化剂通常由附着在其上的金属催化剂纳米颗粒组成。不同材料的支持，并且可以呈现几乎无限数量的配置、形状、成分和工艺操作条件，纯粹通过实验来探索最佳催化剂系统的巨大参数空间是不可能的，因此需要高效的模拟工具。探索催化剂行为该提案旨在开发这些模拟工具，重点是了解催化剂纳米粒子形状在实际反应器操作条件下如何变化；该提案计划使用先进的机器学习方法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准确性，使用本研究项目中开发的模拟工具发现的催化剂将有助于化学过程的脱碳，并将在开发中发挥重要作用。循环化学拟议的研究还将为教育下一代研究人员和行业领导者创造机会，本科生将学习编程技能，从而提高他们在新兴工作领域的竞争力，他们的沉浸式研究经验将为他们在顶尖研究生院的职位做好准备。使用该软件设计的催化剂的宏观渲染将通过 3D 打印生成，以向公众展示计算在加速催化剂设计中的作用。该提案旨在开发和应用从头算机器学习力场。 (AIMLFF) 在现实反应条件下模拟纳米颗粒 (NP) 催化剂，并帮助阐明催化活性位点的性质。这项拟议的研究将通过假设当 AIMLFF 接受周期性密度泛函理论的常见结构特征训练时，专门解决这些挑战。 DFT）计算被社区认为是 NP 催化剂的有意义的表示，AIMLFF 将能够直接在反应相关条件下对 NP 催化剂进行建模。这项工作将解决与 NP 催化剂相关的关键问题。通过与文献中提供的基准质量显微和量热测量进行比较，评估 AIMLFF 的准确性，并将对金属纳米颗粒的形状和可用结合位点如何取决于温度和压力下的金属种类和支撑物有一个总体了解与高质量的 X 射线测量相比，拟议的研究还将更好地了解催化活性和纳米粒子的演化之间的关系，从而获得关于平衡形状和缺陷密度的基础知识。支持的金属纳米粒子改变了对于当前模拟器来说太大的系统的实际反应时间。该奖项反映了 NSF 的法定使命，并且通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。