Xin, Hongliang - virginia tech - Department of Chemical Engineering

个人简介

University of Michigan, Ann Arbor PhD Chemical Engineering 2011 Tsinghua University, Beijing M.S. Chemical Engineering 2005 Tianjin University, Tianjin B.S. Chemical Engineering 2002 Academic Appointments 2014 Assistant Professor, Virginia Tech, Blacksburg. 2013 Postdoctoral Research Fellow, Stanford University/SLAC. 2014 Research areas: The d-band Chemisorption Theory, Dynamic Modeling of Surface Reactions 2011 Postdoctoral Research Fellow, University of Michigan, Ann Arbor. 2013 Research areas: Quantum Chemical Modeling of Electron-driven Reactions, Fuel Cell Catalysis Education 2011• Ph.D. in Chemical Engineering, University of Michigan, Ann Arbor, MI.Advisor: Prof. Suljo Linic Dissertation: First-principles Modeling of the Surface Reactivity of Transition Metals 2005• MSc in Chemical Engineering, Tsinghua University, Beijing, China.Advisor: Prof. Ming-han Han 2002• BSc in Chemical Engineering, Tianjin University, Tianjin, China.Advisor: Prof. Shun-he Zhong

研究领域

Ab Initio Machine Learning Catalysis Theory and Informatics Multiscale Modeling of Catalytic Processes Nonadiabatic Surface Chemistry Electronic Structure Theory and Methods Single Atom/Site Catalysis Electrocatalysis and Photocatalysis

近期论文

查看导师新发文章（温馨提示：请注意重名现象，建议点开原文通过作者单位确认）

Catalyst design with machine learning H. Xin (News & Views) Nature Energy (2022) DOI: 10.1038/s41560-022-01112-8 Machine learning of lateral adsorbate interactions in surface reaction kinetics Tianyou Mou, Xue Han, Huiyuan Zhu, and Hongliang Xin Current Opinion in Chemical Engineering, 36, 2022, 100825 DOI: 10.1016/j.coche.2022.100825 Algorithm-derived feature representations for explainable AI in catalysis N. Omidvar and H. Xin Trends in Chemistry, 2021 DOI: 10.1016/j.trechm.2021.10.001 Bayesian learning of chemisorption for bridging the complexity of electronic descriptors Siwen Wang, Hemanth Somarajan Pillai, and Hongliang Xin* Nat. Commun, 11, 6132 (2020) DOI: 10.1038/s41467-020-19524-z An Adaptive Machine Learning Strategy for Accelerating Discovery of Perovskite Electrocatalysts Zheng Li, Luke E. K. Achenie, and Hongliang Xin ACS Catal. 2020, 10, 7, 4377-4384 DOI: 10.1021/acscatal.9b05248 Ternary PtIrNi Catalysts for Efficient Electrochemical Ammonia Oxidation Yi Li, Xing Li, Hemanth Pillai, et al. ACS Catal. 2020, 10, 7, 3945-3957 DOI: 10.1021/acscatal.9b04670 Elucidation of key factors in nickel-diphosphines catalyzed isomerization of 2-methyl-3-butenenitrile Kaikai Liu, Hongliang Xin, and Minghan Han Journal of Catalysis 377 (2019) 13–19 DOI: 10.1016/j.jcat.2019.07.016 New Insights into Electrochemical Ammonia Oxidation on Pt(100) from First Principles Hemanth Pillai, Hongliang Xin Ind. Eng. Chem. Res., 2019, 58, 25, 10819 DOI: 10.1021/acs.iecr.9b01471 Predicting Catalytic Activity of High-Entropy Alloys for Electrocatalysis Siwen Wang, Hongliang Xin (invited) Chem 5, 502–504 (2019) DOI: 10.1016/j.chempr.2019.02.015 Toward artificial intelligence in catalysis Zheng Li, Siwen Wang, and Hongliang Xin* Nat. Catal., News & Views, 1, 641–642 (2018) DOI: 10.1038/s41929-018-0150-1 Overcoming Site Heterogeneity In Search of Metal Nanocatalysts Wang, Siwen; Omidvar, Noushin; Marx, Emily; Xin, Hongliang* ACS Combinatorial Science (Accepted) DOI: 10.1021/acscombsci.8b00070 Machine Learning Energy Gaps of Porphyrins with Molecular Graph Representations Li, Z., Omidvar, N., Chin, W. S., Robb, E., Morris, A., Achenie, L., and H. Xin* J. Phys. Chem. A, 2018, 122, 18, 4571-4578 DOI: doi:10.1021/acs.jpca.8b02842 Ambient ammonia synthesis via palladium-catalyzed electrohydrogenation of dinitrogen at low overpotential Jun Wang, L. Yu, B. Hu, G. Chen, H. Xin*, and X. Feng* Nat. Commun., 9, 1795 (2018) DOI: doi:10.1038/s41467-018-04213-9 Coordination Numbers for Unraveling Intrinsic Size Effects in Gold-catalyzed CO Oxidation Siwen Wang, Noushin Omidvar, Emily Marx, and Hongliang Xin* Phys. Chem. Chem. Phys., 2018, 20, 6055-6059 DOI: 10.1039/C8CP00102B High-Throughput Screening of Bimetallic Catalysts Enabled by Machine Learning Zheng Li, Siwen Wang, Wei Shan Chin, and Hongliang Xin* J. Mater. Chem. A, 2017, 5, 24131-24138 DOI: 10.1039/C7TA01812F Insights into Electrochemical CO2 Reduction on Tin Oxides from First-principles Calculations Siwen Wang, Jiamin Wang, and Hongliang Xin* Green Energy & Environment, 2017, 2, 2, 168-171 DOI: 10.1016/j.gee.2017.02.005 Orbitalwise Coordination Number for Predicting Adsorption Properties of Metal Nanocatalysts Xianfeng Ma and Hongliang Xin Phys. Rev. Lett. 118, 036101 DOI: 10.1103/PhysRevLett.118.036101 Chemical Bond Activation Observed with an X-ray Laser Martin Beye, Henrik Öberg, Hongliang Xin, et al. J. Phys. Chem. Lett., 2016, 7, pp 3647–3651 DOI: 10.1021/acs.jpclett.6b01543 Analyzing relationships between surface perturbations and local chemical reactivity of metal sites: Alkali promotion of O2 dissociation on Ag(111) Hongliang Xin, and Suljo Linic J. Chem. Phys. 144, 234704 (2016) DOI: 10.1063/1.4953906 Feature Engineering of Machine-Learning Chemisorption Models for Catalyst Design Zheng Li, Xianfeng Ma, and Hongliang Xin Catalysis Today, 280, 232–238 (2017) DOI: 10.1016/j.cattod.2016.04.013 Machine-learning-augmented Chemisorption Model for CO2 Electroreduction Catalyst Screening Xianfeng Ma, Zheng Li, Luke Achenie, and Hongliang Xin J. Phys. Chem. Lett. 6, 3528 ( 2015) DOI: 10.1021/acs.jpclett.5b01660 Strong Influence of Coadsorbate Interaction on CO Desorption Dynamics Probed by Ultrafast X-ray Spectroscopy and Ab Initio Simulations H. Xin, J. LaRue, H. Öberg, et al. Phys. Rev. Lett. 114, 156101 (2015) DOI: 10.1103/PhysRevLett.114.156101 Probing the Transition State Region in Catalytic CO Oxidation on Ru H. Öström, H. Öberg, H. Xin, et al. Science, 1261747 (2015) DOI: 10.1126/science.1261747