(107b) Navigating the Landscape of Advanced Materials

The fourth industrial revolution will be facilitated by the discovery of advanced materials that increase the interconnection of technology and human life. One of the main challenges is identifying promising material candidates for a target application given the virtually limitless number of material possibilities. Computer simulations are an integral tool to survey the material landscape and their performance. In this talk, I will present some of our efforts in the context of metal-organic frameworks (MOFs) which are nanoporous, crystalline materials that combine inorganic and organic building blocks. I will first present on machine learning (ML) efforts to characterize the material landscape and their performance in multiple adsorption applications. I will highlight transfer learning and active learning algorithms to make new predictions at dramatically reduced computational cost, helping navigate the material and thermodynamic conditions required for material performance evaluation. Lastly, I will touch upon the non-traditional application of the developed computational toolset for MOF discovery in quantum optics applications.