A new model to unlock catalytic powers of gold
Xianfeng Ma and Hongliang Xin, Orbitalwise Coordination Number for Predicting Adsorption Properties of Metal Nanocatalysts, Physical Review Letters 118 (2017)
A bimetallic catalyst for electrochemical CO2 reduction to formate
Wesley Luc, Charles Collins, Siwen Wang, et al., Ag–Sn Bimetallic Catalyst with a Core–Shell Structure for CO2 Reduction, Journal of the American Chemical Society, 139 (2017)
Machine Learning In Action
Zheng Li, Xianfeng Ma, and Hongliang Xin, Feature engineering of machine-learning chemisorption models for catalyst design, Catalysis Today 2016.
Accelerating catalyst discovery through machine learning
X. Ma, Z. Li, L. Achenie, and H. Xin, Machine-Learning-Augmented Chemisorption Model for CO2 Electroreduction Catalyst Screening, J. Phys. Chem. Lett, 2015.
First Glimpse of a Chemical Bond Being Born
H. Öström, H. Öberg, H. Xin, et al., Probing the Transition State Region in Catalytic CO Oxidation on Ru, Science, 1261747 (2015)
Welcome to the Xin Group @ Virginia Tech
Title: Rapid Screening of Bimetallic Catalysts Enabled by Machine LearningAuthor(s): Zheng Li, Siwen Wang, Wei Shan Chin, and Hongliang Xin*Source: Journal of Materials Chemistry ADOI: Submitted
Author(s): Siwen Wang, Jiamin Wang, and Hongliang Xin*Source: Green Energy & Environment
Author(s): Wesley Luc, Charles Collins, Siwen Wang, et al.DOI: 10.1021/jacs.6b10435
Author(s): Xianfeng Ma and Hongliang XinSource: Phys. Rev. Lett. 118, 036101
Author(s): Martin Beye, Henrik Öberg, Hongliang Xin, et al.