High-throughput screening of bimetallic catalysts enabled by machine learning
A holistic machine-learning framework shows great promise for accelerating the discovery of bimetallic electrocatalysts for methanol fuel cells by rapidly exploring a broad chemical space.
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.
Welcome to the Xin Group @ Virginia Tech
Author(s): Wang, Siwen; Omidvar, Noushin; Marx, Emily; Xin, Hongliang*
Author(s): Li, Z., Omidvar, N., Chin, W. S., Robb, E., Morris, A., Achenie, L., and H. Xin*
Title: Ambient ammonia synthesis via palladium-catalyzed electrohydrogenation of dinitrogen at low overpotentialAuthor(s): Jun Wang, L. Yu, B. Hu, G. Chen, H. Xin*, and X. Feng*Source: Nat. Commun., 9, 1795 (2018)
Author(s): Siwen Wang, Noushin Omidvar, Emily Marx, and Hongliang Xin*DOI: 10.1039/C8CP00102B