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): Qingqing Guan, Chenghuan Yang, Siwen Wang, et al.Source: ACS Catal., 9, 11146-11152
Title: Elucidation of key factors in nickel-diphosphines catalyzed isomerization of 2-methyl-3-butenenitrileAuthor(s): Kaikai Liu, Hongliang Xin, and Minghan Han
Title: In Situ Formed Pt3Ti Nanoparticles on a Two-Dimensional Transition Metal Carbide (MXene) Used as Efficient Catalysts for Hydrogen Evolution ReactionsAuthor(s): Zhe Li, Zhiyuan Qi, Siwen Wang, et al.Source: Nano Lett. (Accepted)
Author(s): Hemanth Pillai, Hongliang Xin
Author(s): Siwen Wang, Hongliang XinSource: (invited) Chem 5, 502–504 (2019)