DMREF/Collaborative Research: Design of Multifunctional Catalytic Interfaces from First Principles

摘要标题：DMREF：合作研究：通过预测建模和原子尺度实验设计下一代催化剂催化剂是允许生产使现代生活成为可能的关键物质的材料。催化技术对美国经济的许多领域做出了至关重要的贡献，从石化加工到汽车污染减排，许多在当代社会被视为理所当然的产品，如果没有这些关键工艺，就不会存在。传统的发现新型多相催化剂的策略严重依赖于多年的工业实践积累的化学直觉和经验，但要开发下一代催化材料，这些策略将是不够的。由教授组成的协作团队。普渡大学的杰弗里·格里利、沃尔坎·奥塔兰和法比奥·里贝罗以及约翰·霍普金斯大学的王超获得了美国国家科学基金会设计材料计划的资助，以革新和设计我们的未来(DMREF)计划，以制定新的战略。该团队建议通过原子级分辨率的实验和使用大规模计算进行建模的组合来做出准确的预测。这种预测技术已经被探索用于简单类别的催化材料，例如高度有序的金属或氧化物表面。然而，通过探索所谓的“多功能”材料，可以获得更广阔的潜在令人兴奋的催化剂空间，这种材料在金属和氧化物之间提供了复杂的界面。研究人员将结合无与伦比的原子规模的实验表征、合成和反应性测量来为计算模型提供信息，并测试从计算分析中出现的预测催化剂。该计划将为加速鉴定突破性催化材料奠定基础，并为以CO、CO2和H2为原料的反应特别是寻找实用的新催化剂奠定基础。单组分多相催化剂受到催化速率固有限制的限制，许多催化化学实验中观察到的火山图中众所周知的最大值就是例证。反过来，这些局限性又可以追溯到一系列广泛的基本关联，这些关联存在于基本步骤的能量学和有关地点的物种之间。多功能催化结构，如存在于薄膜氧化膜和金属纳米颗粒之间的界面，为克服这些限制和识别全新的催化剂类别提供了潜在的手段。为这种多功能结构开发一个统一的设计框架将需要结合第一原理分子建模技术、在原子尺度上合成和表征催化剂结构的先进方法，以及对所得材料上反应速率的高精度测量。项目团队将专注于与氢气生产和甲醇合成相关的模型反应，这些反应可以在多功能界面上推广。该团队将开发新的分子建模策略，主要依靠从头算方法，以快速评估感兴趣反应的许多金属/氧化物界面组合的催化性能。然后，将使用允许在原子水平上控制催化剂结构的技术来合成从这些计算筛选研究中涌现出来的有前途的候选者。这些催化剂的催化和结构性质将在原子分辨率下进行实验验证，得到的信息将用于改进预测模型和进一步提炼候选材料。最终目标是一种具有广泛适用性的方法，可以用来设计突破性的多功能催化材料，用于各种具有科学和经济意义的反应。

Abstract Title: DMREF: Collaborative Research:Design of next-generation catalysts through predictive modeling and atomic-scale experimentsCatalysts are the materials that allow the production of critical substances that make modern life possible. Catalytic technologies make essential contributions to many sectors of the US economy, ranging from petrochemicals processing to pollution abatement in automobiles, and many products that are taken for granted in contemporary society would not exist without these crucial processes. Traditional strategies for the discovery of new heterogeneous catalysts have relied heavily on chemical intuition and experience accumulated over many years of industrial practice, but to develop the next generation of catalytic materials, these strategies will be inadequate. The collaborative team of Profs. Jeffrey Greeley, Volkan Ortalan, and Fabio Ribeiro of Purdue University, and Chao Wang of Johns Hopkins University, have been awarded a grant under the National Science Foundation Designing Materials to Revolutionize and Engineer our Future (DMREF) initiative to develop a new strategy. The team proposes to make accurate predictions from a combination of experiments with atomic-level resolution and modeling using large-scale computing. Such predictive techniques have been explored for simple classes of catalytic materials, such as highly ordered metal or oxide surfaces. However, a much broader space of potentially exciting catalysts can be accessed by exploring so-called "multifunctional" materials, which offer complex interfaces between metals and oxides. The researchers will combine unparalleled atomic-scale experimental characterization, synthesis, and reactivity measurements to both inform the computational models and test predicted catalysts to emerge from the computational analysis. The proposed program will both lay the fundamental groundwork for accelerated identification of breakthrough catalytic materials, in general, and identify practical new catalysts for reactions with CO, CO2, and H2 as feedstocks, in particular.Single component heterogeneous catalysts are constrained by inherent limitations in catalytic rates, as exemplified by the well-known maxima in volcano plots that have been observed for many catalytic chemistries. The limitations can, in turn, be traced to an extensive series of fundamental correlations that exist between the energetics of elementary steps and species on the sites in question. Multifunctional catalytic structures, such as the interfaces that exist between thin oxide films and metal nanoparticles, provide a potential means of overcoming these limitations and identifying entirely new classes of catalysts. Developing a unified design framework for such multifunctional structures will require a combination of first principles molecular modeling techniques, advanced methods to synthesize and characterize the structure of catalysts at the atomic scale, and highly accurate measurements of reaction rates on the resulting materials. The project team will focus on model reactions, relevant to hydrogen production and methanol synthesis, which can be promoted at multifunctional interfaces. The team will develop new molecular modeling strategies, relying primarily on ab-initio methods, to rapidly evaluate the catalytic properties of many combinations of metal/oxide interfaces for the reactions of interest. Promising candidates to emerge from these computational screening studies will then be synthesized using techniques that permit control of the catalyst structure at the atomic level. The catalytic and structural properties of these catalysts will be verified experimentally at atomic resolution, and the resulting information will be used to improve the predictive models and to further refine the candidate materials. The end goal is a method of broad applicability that can be used to design breakthrough multifunctional catalytic materials for a variety of reactions of scientific and economic importance.