Prof. Xiao-Yan Li (Xiaoyan LI)
Dr. Xiaoyan LI earned her Ph.D. in Particle Physics and Nuclear Physics under the mentorships of Prof. Yi Gao & Prof. Beien Zhu at SIANP. She subsequently conducted postdoctoral research at the University of Toronto (Canada) and Northwestern University (USA) with the supervision of Prof. Edward H. Sargent.
Her research focuses on the development and integration of advanced computational methods—including density functional theory (DFT), kinetic Monte Carlo (KMC), ab initio and force field-based molecular dynamics (MD), and machine learning (ML)—to address challenges at the intersection of chemistry, physics, materials science, and sustainable energy. She is particularly interested in the dynamic interplay between catalytic materials and heterocatalysis/ electrochemical reactions, with a focus on CO2 capture and reduction, the oxygen evolution reaction (OER), and multi-carbon coupling processes for value-added chemical production.
Dr. LI has authored more than 45 peer-reviewed publications in top-tier journals, including Science, Nature Catalysis, Nature Nanotechnology, Nature Energy, and Nature Communications, with over 13 as first or co-first author. She continues to drive innovation in computational catalysis and electrochemical energy conversion through the development and application of cutting-edge AI and machine learning techniques.
Education and Professional Experience
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Aug. 2025 - present
Assistant Professor, Department of Chemistry, National University of Singapore
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Jun. 2023 - May 2025
Postdoc Researcher, Department of Chemistry, Northwestern University
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Jun. 2021 - May 2023
Postdoc Researcher, Electrical & Computer Engineering, University of Toronto
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Sep. 2016 - Jun. 2021
Ph.D., Shanghai Institute of Applied Physics, Chinese Academy of Sciences
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Sep. 2016 - Jun. 2017
Visiting Graduate Student, University of Science and Technology of China
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Sep. 2012 - Jun. 2016
B.S. in Physics, Sichuan Normal University
Honors and Awards
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GLOW 2025 Travel Award, Nanyang Technological University
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Excellent Academic Report Award, Chinese Chemical Society
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Outstanding Doctoral Graduates of Shanghai, Shanghai
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National Scholarship for Doctoral Graduate, Chinese Minister of Education
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ZhuLiYueHua Scholarship for Excellent Doctoral Graduate, Chinese Academy of Sciences
Research Highlights
Dr. LI focuses on developing and integrating advanced computational methods such as density functional theory (DFT), kinetic Monte Carlo (KMC), ab initio and force field-based molecular dynamics (MD), and machine learning (ML) across the interdisciplinary fields of physics, materials science, chemistry, and energy environments. The final research goal is to bridge the gap between theoretical modeling and experimental practices in clean energy conversion, utilization, and storage. She is also dedicated to advancing the understanding of the physical & chemical properties of nanomaterials and accelerating the discovery and design of next-generation functional materials for clean energy. The materials have been investigated include metals/alloys, metal oxides, 2D materials, quantum dots, and COFs/MOFs. Through interdisciplinary collaborations, I have contributed to 45 publications in high-impact journals like Science (2), Nat. Catal. (6), Nat. Nanotechnol. (1), Nat. Energy (1), J. Am. Chem. Soc. (1), and others, accumulating 1750 citations with an h-index of 17. Key achievements include:
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Advancing Computational Simulation of Dynamic Reconstruction in Metal-based Nanoparticles (6/2017 – 6/2021)
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Decoded the atomic mechanism of reconstructed TiO2 under water vapor conditions.
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Developed all-atom KMC models to simulate real-time dynamic reconstruction of nanoparticles induced by temperature and environmental conditions.
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Co-developed a user-friendly MOSP package to simulate and predict changes in nanoparticle shape and reactivity under operando conditions.
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Understanding Physical & Chemical Mechanisms for Carbon Neutralization (6/2021 – 6/2023)
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Identified coupling effects of cations and anions in the electrolyte on CO2/CO reduction into multicarbon (C2+) products.
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Identified key descriptors of metal-based nanomaterials for high-throughput screening of activity and selectivity.
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Developed ML model informed by quantum-inspired similarity analysis for high-entropy alloys.
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Developing ML-Assisted Methods for Discovery and Design of Nanomaterials (6/2023 – present)
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Developed a Bayesian optimization framework for iterative database screening, effectively balancing exploration and exploitation.
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Integrate ML into high-throughput screening and provided physical insights for multi-metal oxides design and application for oxygen evolution reaction.