Tairan Wang

Tairan Wang

Hi! I am a Chemical Engineering PhD student at King Abdullah University of Science and Technology beginning from 2020. My supervisor is Prof. Mani-Sarathy. Prior to KAUST, I got my M.S degree in Physical Chemistry supervised by Prof. Weixue Li and B.S degree in Physical Materials both at University of Science and Technology of China.

My research interest includes computational chemistry, machine learning and large language models for heterogeneous catalysis, chemical mechanism and materials design. If you are seeking any form of academic cooperation, please feel free to email me at tairan.wang@kaust.edu.sa.

📝 Publications

(* denotes equal contribution)

📚 LLM for chemistry

  • submitted to AAAI 2024 ScholarChemQA: Unveiling the Power of Language Models in Chemical Research Question Answering, Xiuying Chen*, Tairan Wang*, Jurgen Schmidhuber, et al.
  • To be submitted Catalysis-Specific Large Language Model (ChemLLM): Information Retrieval for Catalyst Evaluation, Catalyst Recommendation and the Design of High Sintering-Resistance Nanocatalyst, Tairan Wang, Weixue Li.
  • Ongoing Data Extraction and Multitask Machine Learning in Electrolysis Catalyst Design, Tairan Wang, Mani Sarathy.
  • Ongoing Electrolyte Design for Sodium Batteries Using Text Mining and Deep Learning, Tairan Wang, Yunpei Zhu.
  • Ongoing Mapping the Chemical Reaction Space with Natural Language Processing and Reaction Graph for Combustion Modeling, Tairan Wang, Mani Sarathy.

🧑‍🎨 Machine learning prediction

  • Nat. Catal. Under review Interpretable Machine Learning for Derivable Equation Discovery of Metal-Support Interaction in Nanocatalysts, Tairan Wang, Sulei Hu, et al.
  • Fuel Under review Transfer Learning and Graph Neural Network Approach to Multitarget Temperature-Dependent Thermochemistry Prediction, Tairan Wang*, Sirio Brunialti*, et al.
  • J. Am. Chem. Soc. 2022 Quantitatively Determining Surface–Adsorbate Properties from Vibrational Spectroscopy with Interpretable Machine Learning, Xijun Wang, Shuang Jiang, Wei Hu, Tairan Wang, et al.
  • Chin. J. of Chem. Phys. 2020 Machine-learning Adsorption on Binary Alloy Surfaces for Catalyst Screening, Tairan Wang, et al.
  • J. Uni. Sci. Tech. Chin. 2020 AI-based Descriptor for Predicting Alloy Formation Energys, Jiancong Li*, Tairan Wang*, et al.

🎙 Computational reaction mechanism

  • Combus. Flame 2023 Computational Thermochemistry of Oxygenated Polycyclic Aromatic Hydrocarbons and Relevant Radicals, Tairan Wang, Yalamanchi Kiran, et al.
  • Proc. Combust. Inst. To be submitted Hydrogen Abstraction Kinetics in Low-Temperature Combustion: A Theoretical Study of 2-methylhexane + OH and 2,4-dimethylpentane + OH Reactions, Tairan Wang*, Myriam Belmekki*, et al.
  • J. Phys. Chem. A. To be submitted Multi-structural Torsional Variational Transition State Theory Analysis of Hydrogen Abstraction Kinetics between n-heptane and Hydroxide Radicals, Myriam Belmekki*, Tairan Wang*, et al.
  • Appl. Energy Combust. Sci. 2022 Large-Scale Thermochemistry Calculations for Combustion Models, Yalamanchi Kiran, Yang Li, Tairan Wang, et al.
  • Combus. Flame 2022 Accurate Thermochemistry Prediction of Extensive Polycyclic Aromatic Hydrocarbons (PAHs) and Relevant Radicals, Yang Li*, Tairan Wang*, et al.

📒 Theoretical catalysis

  • Science To be submitted Structural Dynamics of Interface-Confined Ferrous Centers for Catalytic Oxidation, Yangsheng Li*, Tairan Wang*, Chuwei Zhu, Weixue Li, Fan Yang, Xinhe Bao.
  • Appl. Catal. B 2023 Insight into Oxygen Vacancies of PMoA-TiO2 Catalyst on Deep Oxidative Desulfurization of Fuel Oil and Molecular Characterization of Sulfur Compounds, Jiyuan Fan*, Tairan Wang*, et al.
  • Sep. Purif. Technol. 2023 Oxidative Desulfurization of Fuel Oil and Molecular Characterization of the Sulfone Compound Distribution in the Different Extractants, J Fan, HA Khan, Tairan Wang, et al.

🎖 Honors and Awards

  • 2023 Dean’s Award, KAUST
  • 2022 Quanlification Exam Assistant, KAUST
  • 2020 Outstanding graduate, USTC
  • 2019 Outstanding Teaching Assistant, USTC
  • 2018 The First Prize Scholarship, USTC
  • 2018 National Scholarship, USTC
  • 2017 The Second Prize Scholarship, USTC

💬 Invited Talks and Tutorials

  • 2023.07, Catalysis-Specific Large Language Model: Data Extraction and Design of Electrolysis Catalyst, Aramco
  • 2023.04, Statistical Study of Sintering-Resistance Bifunctional Catalysts with Large Language Models, University of Science and Technology of China
  • 2023.02, Knowledge retrieval from catalysis literature using natural language processing， University of Science and Technology of China
  • 2022.11, Transfer Learning and Graph Neural Network Approach to Multitarget Temperature-Dependent Thermochemistry Prediction，King Abdullah University of Science and Technology

⭐ Professional Affiliations

  • The Combustion Institute
  • Chinese Chemical Society
  • American Chemical Society