| Scope |
There is an expanding list of opportunities for employing statistics, machine learning and neural networks, together referred to as Deep Learning, for the discovery of new materials. This symposium will bring together a diverse collection of researchers working who are employing these emerging computational tools for materials like semiconductors and for applications like electronics, memory, and energy devices. Primary focus will be on discussing how such tools can enhance synthesis, characterization, and device fabrication, testing processes. In 2020, MS&T will come back to Pittsburgh which is home to Carnegie Mellon's top Department of Machine Learning in the School of Computer Science. In addition to speakers from the materials and electronics communities, we will have guest speakers from Carnegie Mellon who specialize in design of experiments, optimizations, as well as novel algorithm design especially for working with small datasets like the ones seen in many materials science experiments). |