His research at Argonne focuses on the use of machine learning to bridge the electronic, atomistic, and mesoscopic scales. He uses supervised machine learning techniques to develop first-principles-based force fields for simulating reactive and mesoscopic systems. Other interests include machine learnt computational tool development for integrated X-ray imaging of ultrafast energy transport across solid–solid and solid–liquid interfaces. His interests span a diverse range of applications from tribology and corrosion to neuromorphic computing and thermal management