양종희 연구실

High-throughput robotic synthesis and characterizations allow for a comprehensive understanding of the materials system, which broadens its physical and chemical insights that pave the way for materials designing with desired functionalities. Machine learning enables prompt and comprehensive data interpretation across a massive dataset, where human cannot catch and explore. This can significantly saves the precious time and efforts of human to be used in a more valuable tasks. Conventional materials characterization relies on a global analysis in an ensemble system. In fact, there are significant local phase/materials homogeneities in a materials system that are associated with chemical complexity. Various state-of-the-art spatially resolved characterizations allow us to understand a comprehensive understanding of the materials, which cannot be identified in the conventional, ensemble-based global characterizations.