Rational Design of Catalysts

Developing new and improved catalysts is crucial for enhancing energy efficiency, promoting environmental sustainability, optimizing resource utilization, enabling novel chemical transformations, boosting economic competitiveness, and advancing fundamental scientific understanding. Catalysts facilitate chemical reactions at lower temperatures, reducing energy input and greenhouse gas emissions across various industries. They enable cleaner processes, such as converting biomass to biofuels or selectively removing pollutants, while improving reaction selectivity and yield, minimizing waste. Novel catalysts unlock previously inaccessible transformations, paving the way for new materials, pharmaceuticals, and valuable compounds. Improved catalysts provide companies with a competitive edge by reducing costs, increasing yields, and enabling high-value product development.

Exploration of catalyst reaction mechanisms is of fundamental importance in improving known catalysts or designing new catalysts. In recent years, a great number of the catalytic processes have been computationally studied using Density Functional Theory (DFT) calculations and other relative methods. Along with the development of computational chemistry methods, parallel computing and high-performance computing clusters, state-of-the-art computational chemistry researches not only uncover the essence of a known catalytic process but also are used as a fast and low-cost pre-screening technique to assist new catalyst design. Notably, the integration of artificial intelligence (AI) and machine learning (ML) approaches has significantly accelerated catalyst development efforts. AI algorithms can learn from the vast datasets generated through computational chemistry calculations and develop predictive models to rapidly identify promising catalyst candidates, thereby optimizing the resource-intensive process of catalyst discovery. This synergistic combination of computational chemistry and AI methodologies has revolutionized the catalyst development process, enabling more efficient exploration of chemical spaces and faster identification of innovative, high-performance catalysts for a wide range of applications. Currently, we are focusing on the following projects:

i. Mechanistic research on propane dehydrogenation to propylene: In order to gain deeper understanding of dehydrogenations of saturated hydrocarbons, we selected propane dehydrogenation reactions as our model reactions, which was catalyzed by Pt based catalyst. To facilitate determination of systematic trends in the propane dehydrogenationon Pt based catalyst, we will first investigate propane dehydrogenation on flat and stepped Pt and PtM alloy surfaces as well as interfaces of metal oxides, including MgO, Al2O3, and TiO2. They were selected for initial study as supports with at least one surface which matches the shape and size of the Pt(100) or Pt(111) surface. In a practical sense, the matched lattice dimensions provide a more uniform geometry between systems, thus facilitating initial development of correlations and Brønsted–Evans–Polanyi (BEP) relationships at the metal/support interfaces. By systematically permuting the alloy metal and nature of the substrates, we will establish correlations and reactivity patterns, including the development of BEP relationships. In the future, the analysis can be extended to other supported transition metal catalysts, and “volcano” relationships can be constructed between the predicted activity of different metal alloy/support structures and key catalytic parameters that are identified through the analysis.

ii. Development of Global Optimization Algorithm: One challenge to build a realistic catalyst model is to determining its most stable structure (global minimum) or set of lowest energy structures of a catalyst under reaction conditions. Although local minimum optimization technics have already been well developed, it could not guarantee the optimized structure to be the most stable one in case with complicated environment, like high adsorbate coverage, surface reconstruction, etc. Ideally, the global minimum can be located by exploring the whole potential energy surface with conducting numbers of local optimizations. We are interested in improvement of global optimization efficiency by reducing the number of local optimizations to obtain the desired global minimum with advanced computational algorithms like genetic algorithm, machine learning.

iii. Automated Review Generation: With the rapid growth of scientific literature, it has become increasingly challenging for researchers to keep up with the latest developments and extract key insights efficiently. To address this, we are developing an automated review generation method based on Large Language Models (LLMs). By leveraging the powerful natural language processing capabilities of LLMs, our approach aims to streamline the literature review process and provide researchers with concise, comprehensive, and up-to-date summaries of the state-of-the-art in various catalysis subfields. Through this project, we aim to develop a powerful tool that enhances the efficiency and quality of literature reviews in catalysis research, ultimately accelerating the discovery and optimization of novel catalytic materials. The automated review generation method has the potential to transform the way researchers engage with the rapidly expanding body of scientific literature, fostering interdisciplinary collaborations and driving innovation in the field of catalysis.