Materials Genome Initiative (MGI) promises to expedite materials discovery through high-through computation and high-throughput experiments. Application of Artificial-intelligence (AI) tools such as machine-learning, deep-learning and various optimization techniques are critical to achieving such a goal.
Some of the key areas of applications in employing AI techniques to materials are: developing well-curated and diverse datasets, choosing effective representation for materials, inverse materials design, integrating autonomous experiments and theory, and choosing appropriate algorithm/work-flow. The idea of including physics-based models in the AI framework is also fascinating. Lastly, uncertainty quantification in AI based predictions for material properties and issues related to building infrastructure for disseminating AI knowledge are of immense importance for making AI based investigation of materials successful. This workshop is intended to cover all the above-mentioned challenges. To make the workshop as effective as possible we plan to mainly focus on inorganic solid-state materials, but are not limited by it.
Topics addressed in this workshop will include (but not be limited to):
Confirmed speakers: Simon Billinge (Columbia Univ), Shyue Ping Ong (UCSD), Tian Xie (MIT), Heather Kulik (MIT), Tim Mueller (Johns Hopkins), Jorg Behler (University of Goettingen), Trevor David Rhone (RPI), Kedar Hippalgaonkar (NTU/A*STAR), Boris Kozinsky (Harvard), Jacqueline Cole (Cambridge), Geoffrey Hautier (Dartmouth), Adama Tandia (Corning Inc.), Maria Chan (ANL), Wei Chen (Northwestern), Rama Vasudevan (ORNL), Zachary Ulsisi (CMU), Raymundo Arroyave (TAMU), Ekin Dogus Cubuk (Google), Chris Rackauckas (MIT), Keith Butler (UKRI)