Materials are an important contributor to technological progress, and yet the process of materials discovery and development has historically been inefficient. In general, the current innovation workflow is human-centered, where researchers design, conduct, analyze and interpret results obtained through experiments, simulations or literature review. Such results are often high-dimensional, large in number and heterogeneous in nature, which hinders a researcher’s ability to draw insight from this data manually.
This webinar explores the synthesis of machine learning with materials research, highlighting a broad spectrum of topics in which machine learning, artificial intelligence, or statistics play a significant role in addressing problems in experimental and theoretical materials science. It also generated discussion on the fundamental connection between machine learning and material science, and its future application and impact.