Machine learning for materials design: a case study in fluorescent metal clusters

Advances in experimental science and data management are allowing researchers to amass larger and larger data sets. In the context of materials physics, how can we explore the ever-growing data to better understand the systems we study and to guide discovery of new materials? This talk presents a case study for data-driven materials design: fluorescent silver clusters stabilized by DNA. Composed of just ~10-30 silver atoms, these clusters exhibit fluorescence colors spanning the visible to near-infrared spectrum that are selected by the sequence of the stabilizing DNA strand. Exactly how sequence selects cluster size and thus color is unknown, limiting promising applications of these materials for biosensing and photonics. I will discuss how we are combining high-throughput experiments and machine learning to solve the silver cluster “genome” and to design new DNA template sequences that are selective for cluster size and color. This approach to a soft-matter-inorganic hybrid system characterized by an extremely large parameter space exhibits the potential of machine learning and data mining for materials research.