A new machine-learning model developed by a West Virginia University student has potential applications in the energy, environmental and health-care fields. The model, which can be used to predict adsorption energies -- i.e., adhesive capabilities in gold nanoparticles -- was developed by Gihan Panapitiya, a doctoral physics student from Sri Lanka. Gold nanoparticles have historically been used by artists to bring out vibrant colors via their interaction with light. Now they are increasingly used in high-technology applications such as electronic conductors and others. "Machine learning recently came into the spotlight, and we wanted to do something linking machine learning with gold nanoparticles as catalysts," Panapitiya said.

A new machine-learning model developed by a West Virginia University student has the potential for energy, environmental and even healthcare applications. The model, which can be used to predict the adsorption energies, i.e. adhesive capabilities in gold nanoparticles, was developed by Gihan Panapitiya, a doctoral physics student from Sri Lanka. Gold nanoparticles have historically been used by artists to bring out vibrant colors via their interaction with light. Now they are increasingly used in high technology applications, electronic conductors and others. "Machine learning recently came into the spotlight, and we wanted to do something linking machine learning with gold nanoparticles as catalysts," he said.