Fueling the AI Revolution in Materials Science

Fueled by our abilities to compute properties and characteristics orders of magnitude faster than they can be measured and recent advancements in harnessing literature data, the materials science field is entering the era of the fourth paradigm of science: data-driven materials design. The Materials Project (www.materialsproject.org) uses supercomputing and industry-standard software infrastructure together with density functional theory to compute the properties of inorganic materials and their properties, design novel materials and offer the data for free together with online analysis and design algorithms. An increasingly data-hungry community downloads millions of records every day, inspiring a rapid increase in the development of machine learning algorithms for the prediction of materials properties, characteristics, and synthesizability. However, we note that truly accelerating materials innovation also requires rapid synthesis, testing and feedback, seamlessly coupled to existing data-driven predictions and computations. The ability to devise data-driven methodologies to guide synthesis efforts is needed as well as rapid interrogation and recording of results – including ‘non-successful’ ones. This talk will outline the rise of data-driven materials design, showcase successes as well as comment on current pitfalls and future directions.