CAREER: Heterogeneous Catalysis from Correlated Wavefunction Theories

Dr. Hong-Zhou Ye of the University of Maryland, College Park is supported by an award from the Chemical Theory, Models and Computational Methods program in the Chemistry Section to develop new theoretical methods for accurately and efficiently simulating chemical reactions catalyzed by transition metal surfaces. Understanding surface reactions at the atomic level is essential for designing improved catalysts with higher energy efficiency, better chemical selectivity, and improved synthesizability, which have critical impacts on the energy, technology, and manufacturing sectors of the U.S. and global economies. However, existing computational tools often face a trade-off between accuracy and efficiency, limiting their ability to model the large and complex systems relevant to realistic catalytic processes. Dr. Ye and his research group will address this challenge by integrating advances in quantum chemistry theory and software developments to create a new generation of computational tools capable of reliably predicting catalytic reaction mechanisms and energetics. These methods will be implemented in the open-source software package PySCF to ensure broad access. In parallel with the research effort, Dr. Ye and his team will pursue educational and outreach activities aimed at strengthening quantitative and scientific computing skills across multiple educational levels, helping to cultivate the next generation of the STEM workforce. These activities will include hands-on instructional modules and summer research experiences for local high school students, as well as an annual computational chemistry workshop for undergraduate and graduate students at the University of Maryland. The project is directed toward developing a Gaussian-based local correlation framework for metallic solids to enable accurate and efficient simulations of transition-metal-based heterogeneous catalysis using correlated wavefunction theories such as the coupled-cluster theory, thereby complementing the prevailing plane-wave-based density functional theory paradigm in computational materials science. Dr. Ye and his team will address two key technical barriers limiting the scalability and accuracy of coupled-cluster methods for transition-metal surfaces by developing (i) a linear-scaling local coupled-cluster theory suitable for metallic systems and (ii) correlation-consistent Gaussian basis sets specifically tailored for transition-metal solids to improve accuracy and numerical stability. The resulting methods and basis sets will be rigorously benchmarked against experimental surface reference data and applied to investigate the reaction mechanisms underlying the industrial Fischer–Tropsch process for synthetic fuel production. Through open-source release of the developed tools and basis sets, this project will lower the barrier to high-level electronic-structure calculations for a broad range of materials systems beyond catalysis, thereby amplifying their impact by enabling the development of systematically improvable approximate theories and fostering synergy with modern data-driven and machine-learning approaches. 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.