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
Title: Nanoporous V-Doped Ni5P4 Microsphere: A Highly Efficient Electrocatalyst for Hydrogen Evolution Reaction at All pHAuthor(s): Yuan Rao, Siwen Wang, Ruya Zhang, et al.
Title: Monodisperse PdSn/SnOx core/shell nanoparticles with superior electrocatalytic ethanol oxidation performanceAuthor(s): Qiang Gao, Tianyou Mou, Shikai Liu, et al.DOI: 10.1039/D0TA08693B
Title: An Adaptive Machine Learning Strategy for Accelerating Discovery of Perovskite ElectrocatalystsAuthor(s): Zheng Li, Luke E. K. Achenie, and Hongliang Xin
Author(s): Yi Li, Xing Li, Hemanth Pillai, et al.
Author(s): Qingqing Guan, Chenghuan Yang, Siwen Wang, et al.