(2ea) Computational and AI-Driven Chemistry for Advanced Heterogeneous Catalyst Design

Rational design of heterogeneous catalysts for energy and sustainability issues has long been a grand challenge in the community of chemical engineering. Despite tremendous progress, development of highly active, selective, and enduring catalysts still primarily relies on heuristic-based or trial-and-error approaches. Over the past decade, the rapid advance of computational chemistry, typically at first-principles level, offers an efficient, accurate, and cost-effective toolkit for revealing the underlying structure-propertity relationships and promising reaction mechanisms, which not only helps rationalize experimental observations but also inspires new directions for experimental studies. Most recently, machine learning, a data-driven technique that is superior in finding hidden correlations, has been increasingly exploited together with first-principles calculations in many research studies for mining physical insights, extracting new descriptors for rapid screening of catalysts, and generating insightful predictions in materials properties. These efforts establish a new research paradigm for accelerated catalysts design and discovery.

The primary goal of my independent research program is to develop and exploit multiscale modeling methods, including density functional theory (DFT) and ab initio molecular dynamics (AIMD), and machine learning to help design high-performance heterogeneous catalysts for various crucial applications such as selective partial oxidation (POx) of hydrocarbons, CO₂ capture, and water splitting, with close collaborations with experimentalists. Specifically, I will focus on three directions: (1) Develop experimentally measurable and theoretically computable descriptors for establishing structure-property relationships, aiming for laying the groundwork for future design of advanced catalysts and materials; (2) Develop novel high-throughput DFT calculation + machine learning protocol supplemented with experimental validation for accelerated materials screening, aiming for expanding materials design space while reducing experimental trial-and-error; (3) Conduct computational studies to investigate practical problems associated with thermal-/photo-/electro-catalysis with a main focus on the fundamental understanding of reaction mechanisms.

My past research work includes significant contributions in a wide variety of projects, for example:

1) Exploiting machine learning neural network models to develop electric dipole-related descriptors for catalytic surface-adsorbates interactions. This work [1] was selected as the “editor’s choice” in Science, 2020, 368, 727-728. The proposed descriptors were recommended as a “promising new type of catalytic descriptor”.

2) Performing high-throughput DFT calculations + machine learning studies to accelerate perovskite discovery for chemical looping air separation, CO₂/H₂O splitting, and methane POx. [2] The effectiveness of this approach has been experimentally validated with many of the predicted perovskites outperforming the previous benchmark by a factor of >2.

3) For the first time, establishing a machine learned quantitative relationship between IR and Raman spectroscopy and catalytic properties including adsorption energy and charge transfer. [3] This study greatly broadens the utility of spectroscopic tools in the context of catalysis.

4) Conducting computational research on a wide range of physical or chemical applications, including photo-/electro-/thermal-catalysis and functional materials, as exemplified by [1-16], to gain new insights and accelerate novel materials’ design and discovery.

Overall, I have contributed computational modelings in many interdisciplinary projects. I have published 60+ papers in prestigious journals such as J. Am. Chem. Soc., Energy Environ. Sci., Nat. Commun., Angew. Chem. Int. Ed., Adv. Mater., etc. I have served as reviewer for 30+ prestigious peer-reviewed journals, including Phys. Rev. Lett., Nat. Commun., Adv. Theory Simul., etc.

Selected publications (Published: 60 total, 8 first author, 18 co-first author as denoted by †)

[1] X. Wang, S. Ye, W. Hu, E. Sharman, R. Liu, Y. Liu, Y. Luo, J. Jiang, “Electric Dipole Descriptor for Machine Learning Prediction of Catalyst Surface−Molecular Adsorbate Interactions”, J. Am. Chem. Soc., 2020, 142, 7737-7743.

[2] X. Wang, Y.Gao, E. Krzystowczyk, S. Iftikhar, J. Dou, R. Cai, H. Wang, C. Ruan, S. Ye, F. Li, “High-Throughput Oxygen Chemical Potential Engineering of Perovskite Oxides for Chemical Looping Applications”, Energy Environ. Sci., 2022, 15, 1512-1528.

[3] X. Wang, S. Jiang, W. Hu, S. Ye, T. Wang, F. Wu, L. Yang, X. Li, G. Zhang, X. Chen, J. Jiang, Y. Luo, “Quantitatively Determining Surface-Adsorbate Properties from Vibrational Spectroscopy with Interpretable Machine Learning”, J. Am. Chem. Soc., 2022, Under review.

[4] X. Wang, E. Krzystowczyk, J. Dou, F. Li, “Net Electronic Charge as an Effective Electronic Descriptor for Oxygen Release and Transport Properties of SrFeO₃-based Oxygen Sorbents”, Chem. Mater., 2021, 33, 2446–2456.

[5] X. Wang, X. Jiang, E. Sharman, L. Yang, X. Li, G. Zhang, J. Zhao, Y. Luo, J. Jiang, “Isolating Hydrogen from Oxygen in Photocatalytic Water Splitting with Carbon-Quantum-Dot/Carbon-Nitride Hybrid”, J. Mater. Chem. A, 2019, 7, 6143-6148.

[6] X. Wang, G. Zhang, Z. Wang, L. Yang, X. Li, J. Jiang, Y. Luo, “Metal-enhanced hydrogenation of graphene with atomic pattern”, Carbon, 2019, 143, 700-705.

[7] X. Wang, G. Zhang, Y. Li, E. Sharman, J. Jiang, “Material descriptors for photocatalyst/catalyst design”, WIREs Comput. Mol. Sci., 2018, e1369.

[8] X. Wang, Z. Wang, J. Jiang, “Insight into Electronic and Structural Reorganizations for Defect-Induced VO₂ Metal-Insulator Transition”, J. Phys. Chem. Lett., 2017, 8, 3129−3132.

[9] M. R. Mian†, X. Wang†, X. Wang, K. O. Kirlikovali, K. Ma, K. M. Fahy, R. Q. Snurr, T. Islamoglu, O. K. Farha, “Structure–Activity Relationship Insights for Organophosphonate Hydrolysis at Ti(IV) Active Sites in Metal–Organic Frameworks”, J. Am. Chem. Soc., 2022, Under review.

[10] Y. Gao†, X. Wang†, N. Corolla, T. Eldred, A. Bose, W. Gao, F. Li, “Alkali metal halide coated perovskite redox catalysts for anaerobic oxidative dehydrogenation of n-butane”, Sci. Adv., 2022, Accepted.

[11] C. Ruan†, X. Wang†, C. Wang, L. Li, J. Lin, X. Liu, F. Li, X. Wang, “Selective catalytic oxidation of ammonia to nitric oxide via chemical looping”, Nat. Commun., 2022, 13, 718.

[12] A. Han†, X. Wang†, K. Tang, Z. Zhang, C. Ye, K. Kong, H. Hu, L. Zheng, P. Jiang, C. Zhao, Q. Zhang, D. Wang, Y. Li, “Adjacent Atomic Pt Site Enables Single-Atom Iron with High Oxygen Reduction Reaction Performance”, Angew. Chem. Int. Ed., 2021, 60, 19262-19271.

[13] A. Han†, X. Zhou†, X. Wang†, S. Liu, Q. Xiong, W. Zhang, F. Li, D. Wang, L. Li, Y. Li, “One-Step Synthesis of Single-Site Vanadium Substitution in 1T-WS₂ Monolayers for Enhanced Hydrogen Evolution Catalysis”, Nat. Commun., 2021, 12, 709.

[14] Y. Zhao†, X. Wang†, S. Yang, E. Kuttner, A. Taylor, R. Salemmilani, X. Liu, M. Moskovits, B. Wu, A. Dehestani, J. Li, M. Chisholm, Z. Tian, F. Fan, J. Jiang, G. Stucky, “Protecting the Nanoscale Properties of Ag Nanowires with an Epitaxially Grown Single SnO₂ Monolayer as Corrosion Inhibitor”, J. Am. Chem. Soc., 2019, 141, 13977-13986.

[15] V. P. Haribal†, X. Wang†, R. Dudek, C. Paulus, B. Turk, R. Gupta, F. Li, “Modified Ceria for “Low-Temperature” CO₂ Utilization: A Chemical Looping Route to Exploit Industrial Waste Heat”, Adv. Energy Mater., 2019, 9, 41, 1901963.

[16] N. Du†, C. M. Wang†, X. Wang†, Y. Lin, J. Jiang, Y. Xiong, “Trimetallic TriStar Nanostructures: Tuning Electronic and Surface Structures for Enhanced Electrocatalytic Hydrogen Evolution”, Adv. Mater., 2016, 28, 2077-2084.

Teaching Interests:

“Teaching is more than imparting knowledge, it is inspiring change.” --- William Arthur Ward

This quote is a crisp summary of my teaching philosophy. I believe that the amazing thing about teaching is not just about filling students’ brains with book knowledge. More importantly, it’s about finding ways to inspire students to make positive impact on the world around them. I am fortunate enough to dedicate my life to scientific research and education, which has offered me the tools to make the world a better place. I found teaching to be an extremely rewarding thing to pass on these tools and inspire my students to apply them to make a better world.

Based on my previous education and research background, I would be most interested in teaching classes related to Thermodynamics, Kinetics and Reaction Engineering, and Molecular Engineering and Statistical Mechanics. In addition, I would be interested in teaching or developing elective courses such as Applied Molecular Modeling and Machine Learning in Chemistry. I believe that students will benefit from these elective courses because having some experiences with computational and data-driven techniques will be a very valuable addition to their future careers. Of course, I am open and readily to prepare for any required courses to meet the needs of the department.

Selected Awards

• Nominated for the CUSPEA Prize (2021)
• Top Peer Reviewer in Physics awarded by Publons (2019)
• Top Peer Reviewer in Chemistry awarded by Publons (2019)
• Top Peer Reviewer in Cross-Field awarded by Publons (2019)
• Kwang-Hua Scholarship (2017)
• First-order Academic Scholarship in USTC (2016)
• Xingye Responsibility scholarship (2015)
• First-order Academic Scholarship in USTC (2015)
• First-order Academic Scholarship in USTC (2014)