AI research in the CEST group receives funding for Bayesian Optimization of Structure Search
Functional hybrid materials are engineered blends of organic molecules and inorganic crystals that harness and enhance the functional properties of both substances to perform specific functions for novel applications and devices. To control and engineer the functionality of these hybrid materials, we must better understand their microscopic structural details. In the Artificial Intelligence (AI) for Microscopic Structure Search (AIMSS) project we develop AI methodology and combine it with quantum mechanical simulations. The AI technology in our combined framework provides an essential efficiency breakthrough that enables us to predict, for the first time, the microscopic structure of organic-inorganic hybrid materials. We showcase AIMSS for two flagship applications related to key technologies: nanofriction and hybrid optoelectronics.
Caption: AIMSS combines quantum mechanics and machine learning to leap from our current state of the art of simulating one molecule adsorbed on a surface to complex molecular ensembles, such as the dicyano-anthracene CU network depicted on the right.
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