The Materials Genome Initiative (MGI) promises to expedite materials discovery through high-through computation and high-throughput experiments. While the MGI effort has been successful to screen interesting materials among thousands of materials, the possible materials can span up to 10¹⁰⁰ limiting the current MGI philosophy.

One of the possible approaches to deal with this problem is using artificial-intelligence (AI) tools such as machine-learning, deep-learning and various optimization techniques to efficiently evaluate materials performance. Although AI has been very successful in fields such as voice-recognition, self-driving cars, language translation etc., its applicability to materials design is still in its developing phase. Two key challenges in employing AI techniques to materials are: choosing effective descriptors for materials and choosing algorithm/work-flow during AI design. 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.