DMREF: Collaborative Research: Design and Discovery of Multimetallic Hetergeneous Catalysts for a Future Biorefining Industry

海登，安德烈斯/邦德，杰西q . .该提案利用统计分析来估计与琥珀酸催化加氢脱氧（SUCC HDO）有关的实验数据和理论计算的不确定性-这是一个重要的反应，在提炼生物质衍生的化学品到商业价值的产品。所采用的实验和计算方法-结合统计误差分析-为识别改进的催化材料提供了比单独通过实验或理论更准确和强大的方法。该方法适用于广泛的催化应用，可以为发现和设计催化材料的新方法提供蓝图。研究结果将通过网站和软件工具提供给催化社区。具体来说，该项目涉及制备定义明确且分散良好的锡（Sn）双金属团簇，这些锡（Sn）吸附在钌（Ru）、铂（Pt）或铑（Rh）上，沉积在无定形二氧化硅或碳载体上。催化剂将在结构、组成和表面酸度方面进行详细的表征，然后在suc HDO反应中进行评价。多尺度策略将用于基于DFT方法和研究者实验室开发的技术的计算，旨在减少自由能估计中的不确定性。实验和计算分析中的不确定性将服从贝叶斯统计分析。将对实验方法和计算方法进行改进，以尽量减少不确定性，并在理论和实验之间进行有意义的比较。研究中采用的方法可以潜在地指导材料选择和催化剂设计的许多应用，而不仅仅是在这里演示的特定催化剂和反应。为实验和计算工作设定了严格的标准，当与统计分析相结合时，为预测新的催化材料提供了迄今为止缺乏确定性的信心。所选择的反应是在生物质加工中，不仅证明了这些方法在复杂系统中的应用，而且表明了这些方法在水相和气相反应中的潜在应用，这对可再生资源和能源可持续性很重要。

#1534260 / #1534269Heyden, Andreas / Bond, Jesse Q.The proposal utilizes statistical analysis to estimate uncertainties in both experimental data and theoretical calculations relating to catalytic hydrodeoxygenation of succinic acid (SUCC HDO) - an important reaction in the refining of biomass-derived chemicals to commercially valuable products. The experimental and computational methods employed - combined with statistical error analysis - provide a more accurate and powerful approach for identifying improved catalytic materials than possible by either experiments or theory alone. The approach is applicable to a broad range of catalytic applications, and could provide a blueprint for a new approach to the discovery and design of catalytic materials. The results of the study will be made available to the catalysis community via a website and software tool.Specifically, the project involves preparation of well-defined and well-dispersed bimetallic clusters of tin (Sn) adsorbed on ruthenium (Ru), platinum (Pt) or rhodium (Rh) deposited on amorphous silica or carbon supports. The catalysts will be characterized in detail with respect to structure, composition, and surface acidity, and then evaluated in the SUCC HDO reaction. A multiscale strategy will be used for the computations based on DFT methods and techniques developed in the investigators' laboratory aimed at reducing uncertainties in the estimation of free energies. Uncertainties in both the experimental and computational analyses will be subjected to Bayesian statistical analysis. Refinements to both the experimental and computational methods will be made to minimize the uncertainties and obtain meaningful comparisons between theory and experiment. The methodology employed in the study can potentially guide materials selection and catalyst design for many applications beyond the specific catalysts and reaction demonstrated here. Rigorous standards are set for both the experimental and computational work, that when combined with statistical analysis, provide confidence heretofore lacking in the certainty with which new catalytic materials can be predicted. The selected reaction is in biomass processing and not only demonstrates application of the methods to complicated systems, but suggests potential use of the methods in both aqueous and gas-phase reactions important to renewable resources and energy sustainability.