Symposium GI01—Machine Learning and Data-Driven Materials Development and Design
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. Efforts such as the Materials Genome Initiative address this issue through data-driven techniques, increasingly utilizing methods from statistics, machine learning and artificial intelligence to interpret and model materials data. Such techniques have allowed researchers to make predictions of materials structures and properties, perform autonomous research with robot scientists, and speed up computational work. The result is an acceleration of materials discovery, and a strengthened ability to draw scientific insights from complex data. This symposium will explore 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 will also spur discussion on the fundamental connection between machine learning and material science, and its future application and impact. The combination of machine learning with materials science has broad implications for the future of research processes, the roles of humans and intelligent systems in a coordinated human-machine partnership, and ultimately the rate at which materials research and scientific understanding progresses.