Compound Knowledge Graph-Enabled AI Assistant for Accelerated Materials Discovery

Abstract Materials scientists are facing increasingly challenging multi-objective performance requirements to meet the needs of modern systems such as lighter-weight and more fuel-efficient aircraft engines, and higher heat and oxidation-resistant steam turbines. While so-called second wave statistical machine learning techniques are beginning to accelerate the materials development cycle, most materials science applications are data-deprived when compared to the vastness and complexity of the search space of possible solutions. In line with DARPA’s vision of third wave AI approaches, we believe a combination of data-driven statistical machine learning and domain knowledge will be required to achieve a true revolution in materials discovery. To that end, we envision and have begun reducing to practice a system that fuses three forms of knowledge—factual scientific knowledge, physics-based and/or data-driven analytical models, and domain expert knowledge—into a single ‘Compound Knowledge Graph’ in which contextual reasoning and adaptation can be performed to answer increasingly complex questions. We believe this Compound Knowledge Graph-based system can be the nucleus of a collaborative AI assistant that supports stateful natural language back-and-forth dialogs between materials scientists and the AI to accelerate the development and discovery of new materials. This paper details our vision, summarizes our progress to date on a steam turbine blade coating use case, and outlines our thoughts on the key challenges in making this vision a reality.