DMREF: Collaborative Research: Design of surface functionality through surface composition and structure

多组分材料，如合金，在国家的基础设施，制成品，硬件技术和国防设备的关键方面发挥作用。 常见的例子是黄铜（铜和锌的混合物）和不锈钢（铁，碳和其他元素，如铬和镍的混合物）。为特定应用设计合金的主要挑战是了解其有用特性（成本，硬度，密度，耐腐蚀性，化学性质等）取决于其特定的元素组合。 随着元素数量的增加，测试可能的组合范围需要制备和研究呈指数增长的合金样品。 该项目将开发和应用现代研究工具，以大大提高合金性能的实验测量和/或通过计算机模拟预测的速度。 令人感兴趣的应用是催化生产环氧丙烷，一种价值数十亿美元的商品化学品。 研究人员将使用高通量实验方法同时从100种不同的二元或三元合金催化剂组合物中收集催化反应数据，而不是一次一种催化剂组合物。 这些实验方法将与通过快速计算模拟预测催化活性的机器学习方法相结合。 一旦这些计算方法的性能与实验进行了基准测试，它们就可以用于设计具有更复杂组成、结构和形态的合金催化剂。 更重要的是，一旦它们在催化材料设计中的效用被确立，这样的工具就可以应用于各种各样的应用，加速用于各种各样的工程、产品设计、该DMREF项目将利用CuxAgyAu（1-x-y）上的丙烯环氧化反应制备环氧丙烷，催化剂表面，以便在催化表面设计的背景下改进合金选择和优化。这里调查的系统将使有效的定制的变量，如表面取向和结构;元素组成之间的关系，在散装和表面上由于隔离;和操作环境对表面结构和组成的影响。 用于合金催化研究的高通量方法将利用成分扩散合金膜（CSAF）作为材料库，其包含平行于其表面的Cu、Au和Ag的梯度，使得在单个膜上发现许多成分。 结合高通量多通道微反应器阵列，将在100种合金组合物下同时测量催化活性，跨越温度和反应物进料组合物的范围。 此外，还将测量合金的表面组成，以确定偏析对反应性的影响。 这些数据将用于验证催化活性的高通量计算模拟。 将开发机器学习方法，以加快基于密度泛函理论的原子间势的原子模拟，预计这将加速过滤合金表面催化活性位点的许多可能的原子构型的过程。 这些建模方法通知进一步的实验测量的催化活性和原位表征的表面跨越合金组合物空间; CuxAgyAu（1-x-y）与0x 1和0 y1-x。研究人员希望这些研究能够产生新的计算工具，用于预测合金的催化活性，从而实现严格和有效的催化剂优化。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。

Multicomponent materials, such as alloys, serve in critical aspects of the nation's infrastructure, manufactured goods, hardware technologies, and defense apparatus. Common examples are brass (mixture of copper and zinc) and stainless steels (mixtures of iron, carbon and other elements such as chromium and nickel). The main challenge of designing an alloy for a particular application is understanding how its useful characteristics (cost, hardness, density, corrosion resistance, chemical properties, etc.) depend on its specific mix of elements. As the number of elements increases, testing the range of possible combinations requires the preparation and study of an exponentially increasing number of alloy samples. The project will develop and apply modern research tools to vastly increase the rate at which the properties of alloys can be measured experimentally and/or predicted by computational simulation. The application of interest is the catalytic production of propylene oxide, a multibillion-dollar commodity chemical. The investigators will use high-throughput experimental methods to collect catalytic reaction data from 100 different binary or ternary alloy catalyst compositions concurrently, rather than one catalyst composition at a time. These experimental methods will be integrated with machine-learning methods that predict catalytic activity via rapid computational simulations. Once the performance of these computational methods has been benchmarked against experiment, they can be deployed for design of alloy catalysts with more complex compositions, structures and morphologies. More importantly, once their utility in the design of catalytic materials is established, such tools can be applied to a wide variety of applications, accelerating the design of multicomponent alloys for a wide variety of engineering, product design, and hardware technologies.This DMREF project will use the propylene epoxidation to propylene oxide on CuxAgyAu(1-x-y) catalyst surfaces in order to improve alloy selection and optimization in the context of catalytic surface design. The system investigated here will enable efficient tailoring of variables such as surface orientation and structure; the relationship between elemental compositions in the bulk and on the surface due to segregation; and the influence of operational environments on surface structure and composition. High-throughput methods for alloy catalysis study will make use of composition spread alloy films (CSAFs) as material libraries, containing gradients of Cu, Au, and Ag parallel to their surfaces, such that many compositions are found over a single film. Coupled with a high throughput multichannel microreactor array, the catalytic activity will be measured at 100 alloy compositions concurrently, across a range of temperatures and reactant feed compositions. In addition, the surface composition of the alloy will be measured to determine the effects of segregation on reactivity. These data will serve to verify high-throughput computational simulations of catalytic activity. Machine-learning methods will be developed to expedite atomistic simulations based on interatomic potentials derived from Density Functional Theory, which is expected to accelerate the process of filtering the many possible atomistic configurations of catalytically active sites at an alloy surface. These modeling methods inform further experimental measurements of catalytic activity and in-situ characterization of surfaces spanning alloy composition space; CuxAgyAu(1-x-y) with 0x1 and 0y1-x. The investigators expect these studies to yield new computational tools for predicting catalytic activity of alloys, enabling rigorous and efficient catalyst optimization.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.