Group Highlight

  • Infusing theory into deep learning for interpretable reactivity prediction

    A new artificial intelligence framework is developed that can accelerate discovery of materials for important technologies, such as fuel cells and carbon capture devices.

  • Bayesian learning of chemisorption for bridging the complexity of electronic descriptors

    A Bayesian learning approach based on ab-initio adsorption properties and the d-band model captures physics of adsorbate-metal interactions.

  • 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)

Welcome to the Xin Group @ Virginia Tech

Our group focuses on modeling structure-function relationships of nanoscale assemblies for energy and electronics applications. Ongoing research includes design of hybrid materials for catalysis, solar energy capture and storage, charge transport, etc. This research will be supported by a collaborative effort in assembling diverse classes of functional materials on the nanoscale. To guide materials design, special attention has been given to the development of a multiscale modeling framework that integrates our expertise in ab-initio calculations, kinetic simulations, and statistical learning. Motivated by recent advances in ultrafast science, we specifically tackle challenging problems in energy and electronics that require fundamental understanding of atomistic and charge dynamiX at interfaces.