Natural and Artificial Discovery and Design of Porous Materials

Abstract

In this talk, I will explore the paths of designing and discovering novel porous materials using both natural (i.e. computational) and artificial designs. Regarding the natural design, novel composite metal-organic frameworks (MOFs) were designed using our lattice-matching algorithm and our predictions were realized via successful synthesis of various MOF@MOF core shell materials. These materials possess potential synergetic properties for various applications including chemiresistor sensors. Regarding the artificial design, I will talk about developments made using GAN and diffusion models to create porous materials using deep learning and some of the other implementations to take advantage of the modular nature of the MOFs and to build towards universal transfer learning to predict various properties of the MOFs. Within this framework of material prediction models, I will briefly talk about some of the recent work done in integrating GPT type of language models with inverse design of materials.