Published in: npj Computational Materials, 3(1), 1-7, 2 October 2017

Authors

Ioannis Tsamardinos

Giorgos Borboudakis, Taxiarchis Stergiannakos, Maria Frysali, Emmanuel Klontzas, George E. Froudakis

Abstract

A novel computational methodology for large-scale screening of MOFs is applied to gas storage with the use of machine learning
technologies. This approach is a promising trade-off between the accuracy of ab initio methods and the speed of classical
approaches, strategically combined with chemical intuition. The results demonstrate that the chemical properties of MOFs are
indeed predictable (stochastically, not deterministically) using machine learning methods and automated analysis protocols, with
the accuracy of predictions increasing with sample size. Our initial results indicate that this methodology is promising to apply not
only to gas storage in MOFs but in many other material science projects.