OU Pengfei

OU Pengfei

Assistant Professor, NUS Presidential Young Professorship

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Research

ORCID: 0000-0002-3630-0385
ResearcherID: HQY-5669-2023
Google Scholar: https://scholar.google.com/citations?user=RxpZqqgAAAAJ&hl=en

 

Recognition and Achievements

• NUS Presidential Young Professorship, National University of Singapore, 2024
• Climate Positive Energy Postdoctoral Fellowship, University of Toronto, 2021
• Chinese Government Award for Outstanding Self-Financed Students Aboard, China Scholarship Council, 2020
• Graduate Research Excellence Award, McGill University, 2019
• Graduate Research Enhancement and Travel Awards, McGill University, 2018
• Graduate Mobility Award, McGill University, 2018
• Graduate Admission Scholarship, McGill University, 2015

 

Research Interests

• Catalyst design for electrochemical reactions (reaction mechanism, transition states, and reaction thermodynamics and kinetics) using ab initio DFT calculations, cluster expansion, and microkinetic modeling.
• Dynamic simulations (dynamic change of active sites, catalyst degradation) of chemical processes under reaction conditions (applied potential, field/solvation effect) using (ab initio) molecular dynamics simulations and grand canonical DFT.
• Methods development for accelerated catalyst discovery (active learning and Bayesian optimization) using AI4Catalysis, ML, open databases, and data science.

 

Research Highlight

1. High-throughput catalyst design for electrochemical reactions using ab initio DFT calculations, cluster expansion, and microkinetic modelling. References: Nat. Energy 2023, 7, 170−176; J. Am. Chem. Soc. 2023, 145, 7829–7836; Nat Commun 2021, 12, 6089.

2. Dynamic simulations of chemical processes under reaction conditions using (ab initio) molecular dynamics simulations and grand canonical DFT. References: Nat. Catal. 2023, 6, 939–948; Nat. Catal. 2022, 5, 564–570; Sci. Adv. 2022, 8, abm3779

3. Methods development for accelerated catalyst discovery using AI/ML, open databases, and data science. References: https://arxiv.org/abs/2404.12445; Matter, 2024, 7, 4099–4113; J. Mater. Chem. A 2023, 11, 9964–9975

 

Representative Publications

• Zhou, D.; Yu, J.; Tang, J.; Li, X.-Y.; Ou, P. Octahedral Co²⁺-O-Co³⁺ in Mixed Cobalt Spinel Promotes Active and Stable Acidic Oxygen Evolution. Adv. Energy Mater. 2024, 2404007.
• Chang, Y.; Benlolo, I.; Bai, Y.; Reimer, C.; Zhou, D.; Zhang, H.; Matsumura, H.; Choubisa, H.; Li, X.-Y.; Chen, W.; Ou, P.; Tamblyn, I.; Sargent, E. H. High-Entropy Alloy Electrocatalysts Screened Using Machine Learning Informed by Quantum-Inspired Similarity Analysis. Matter 2024, 7, 4099–4113.
• Li, X.-Y.; Duan, M.; Ou, P. High-Throughput Screening of Single-Atom Catalysts Confined in Monolayer Black Phosphorus for Efficient Nitrogen Reduction Reaction. Nano Res. 2024, 17, 2360−2367.
• Zhao, Y.; Chen, Y.; Ou, P.; Song, J. Basal Plane Activation via Grain Boundaries in Monolayer MoS₂ for Carbon Dioxide Reduction. ACS Catal. 2023, 13, 12941–12951.
• Chen, Y.; Zhao, Y.; Ou, P.; Song, J. Basal Plane Activation of Two-Dimensional Transition Metal Dichalcogenides via Alloying for the Hydrogen Evolution Reaction: First-Principles Calculations and Machine Learning Prediction. J. Mater. Chem. A 2023, 11, 9964–9975.
• Xie, Y.; Ou, P.; Wang, X.; Xu, Z.; Li, Y. C.; Wang, Z.; Huang, J. E.; Wicks, J.; McCallum, C.; Wang, N.; Wang, Y.; Chen, T.; Lo, B. T. W.; Sinton, D.; Yu, J. C.; Wang, Y.; Sargent, E. H. High Carbon Utilization in CO₂ Reduction to Multi-Carbon Products in Acidic Media. Nat. Catal. 2022, 5, 564–570.
• Wang, X.; Ou, P.; Ozden, A.; Hung, S. F.; Tam, J.; Gabardo, C. M.; Howe, J. Y.; Sisler, J.; Bertens, K.; García de Arquer, F. P.; Miao, R. K.; O’Brien, C. P.; Wang, Z.; Abed, J.; Rasouli, A. S.; Sun, M.; Ip, A. H.; Sinton, D.; Sargent, E. H. Efficient Electrosynthesis of n-Propanol from Carbon Monoxide Using a Ag–Ru–Cu Catalyst. Nat. Energy 2022, 7, 170−176.