(1) Bing Huang*, Guido Falk von Rudorff*, and O. Anatole von Lilienfeld*. “The central role of density functional theory in the AI age”, Science 381, no. 6654 (2023): 170-175. URL: https://doi.org/10.1126/science.abn3445 

(2) Bing Huang, and O. Anatole Von Lilienfeld. “Ab Initio Machine Learning in Chemical Compound Space”, Chemical Reviews 121, no. 16 (2021): 10001-10036. URL: https://doi.org/10.1021/acs.chemrev.0c01303 

(3) Bing Huang, and O. Anatole von Lilienfeld. “Quantum machine learning using atom-in-molecule-based fragments selected on the fly”, Nature Chemistry 12, no. 10 (2020): 945-951. URL: https://doi.org/10.1038/s41557-020-0527-z (highlighted by chemistryworld: https://www.chemistryworld.com/opinion/a-3d-periodic-table/3007821.article)

(4) B. Huang, L. Xiao, J. Lu, L. Zhuang, “Spatially Resolved Quantification of the Surface Reactivity of Solid Catalysts”, Angewandte Chemie International Edition, 2016, 55, 6239-6243. (hot paper, see here)

(5) B. Huang, L. Zhuang, L. Xiao, J. Lu, “Bond-Energy Decoupling: Principle and Application to Heterogeneous”, Chemical Science, 2013, 4, 606-611. (hot paper, see here)

(6) Bing Huang, and O. Anatole von Lilienfeld. “Communication: Understanding molecular representations in machine learning: The role of uniqueness and target similarity”,  Journal of Chemical Physics, 2016, 145, 161102 (URL: doi.org/10.1063/1.4964627)

(7) Bing Huang , O. Anatole von Lilienfeld and Jaron T. Krogel, Anouar Benali. “Toward DMC Accuracy Across Chemical Space with Scalable Δ-QML”,  Journal of Chemical Theory and Computation,  19,  1711-1721,  2023. (URL: doi.org/10.1021/acs.jctc.2c01058)