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): J. LaRue, O. Krejčí, L. Yu, et al.
Author(s): Zheng Li, Siwen Wang, Wei Shan Chin, and Hongliang Xin*Source: Journal of Materials Chemistry ADOI: 10.1039/C7TA01812F
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