My research focuses on AI + Chemistry, aiming to automate molecule generation and evaluation, and to learn to model chemical processes.

Current Research Area

• Learning-based modeling of chemical processes.
• Language models for chemistry predictions.
• Automatic catalyst design and evaluation.

News

• July 26, 2024: CatScore paper is out at Digital Discovery! We introduce CatScore, a learning-based evaluation metric for asymmetric catalysis in organic chemistry. CatScore enables highly efficient and effective evaluation of diverse catalyst design models at instance and system levels.

Selected Papers

	CatScore: Evaluating Asymmetric Catalyst Design at High Efficiency Bing Yan, Kyunghyun Cho. Digital Discovery. 2024, Advance Article
	Electrochemical Activation of C-C Bonds via Mediated Hydrogen Atom Transfer Reactions Bing Yan, Changxia Shi, Gregg T. Beckham, Eugene Y.-X. Chen, Yuriy Roman-Leshkov. ChemSusChem. 2022, 15, e202102317
	Mixed Electron-Proton Conductors Enable Spatial Separation of Bond Activation and Charge Transfer in Electrocatalysis Bing Yan, Ryan P. Bisbey, Alexander Alabugin, Yogesh Surendranath. J. Am. Chem. Soc. 2019, 141, 11115-11122
	Surface Restructuring of Nickel Sulfide Generates Optimally Coordinated Active Sites for Oxygen Reduction Catalysis Bing Yan, Dilip Krishnamurthy, Christopher H. Hendon, Siddharth Deshpande, Yogesh Surendranath. Joule 2017, 1, 600-612. Referred to by Fang Song, Jordan Katz, Xile Hu in "Catalyst Surface Dynamics Reveals a Simple Geometric Descriptor of Activity" Joule 2017, 1, 421-430.
	A Membrane-Free Neutral pH Formate Fuel Cell Enabled by a Selective Ni3S2 Oxygen Reduction Catalyst Bing Yan, Nolan M. Concannon, Yogesh Surendranath.. Inside Cover. Angew. Chem. Int. Ed. 2017, 56, 7496-7499