Current research Discovery of high-entropy alloys for corrosion protection A machine learning framework to evaluate corrosion performance for given compositions of high-entropy alloys Inverse design of single-phase high-entropy alloys Disentangled representation of compositions/structures and properties in a semi-supervised variational autoencoder (VAE) Perspective of materials design with machine learning Methods and algorithms for machine learning assisted materials design Past research Enabling exascale computing of chemical systems Machine learning potentials for large-size nanoparticle catalysts Eigenforce models to expedite catalyst design Force-displacement models for strain effect, surface relaxation and lateral interaction Data science Machine learning competitions on Kaggle A list of past kaggle competitions Course projects A collection of data science course projects supported by Brown's open graduate education program
