Machine Learning Enables Predictive Modeling of 2-D Materials
Wednesday, December 7, 2016 - 17:51
in Physics & Chemistry
In a study published in The Journal of Physical Chemistry Letters, a team of researchers led by Argonne computational scientist Subramanian Sankaranarayanan described their use of machine learning tools to create the first atomic-level model that accurately predicts the thermal properties of stanene, a 2-D material made up of a one-atom-thick sheet of tin.