The powerful combination of supercomputing resources, robust algorithms for solving the laws of physics, and state-of-the-art software infrastructure are enabling rapid, systematic calculations of real materials properties from quantum mechanics across chemistry and structure. A result of this paradigm change are databases like the Materials Project (www.materialsproject.org) which is charting the properties of all known inorganic materials and beyond, offering the data free of charge to the community together with online analysis and design algorithms.
Today - 8 years after launch, the Materials Project is driving materials innovation in broad chemical and structure spaces, for applications as varied as energy storage, energy production, thermoelectricity, transparent conductors, materials synthesis conditions, etc. The current release contains data derived from quantum mechanical calculations for over 100,000 materials and millions of properties. The software infrastructure enables thousands of calculations per week - enabling screening and predictions - for both novel solid as well as molecular species with target properties.
We will highlight the development of the Project, its growth attracting more than 100,000 users and serving over a million data items through the API every day to the community. The growing body of available, reliable data has reached the stage where automated learning algorithms can be effectively trained and utilized to accelerate all aspects of the materials design cycle: from property prediction, to materials synthesis and characterization.
To exemplify the approach of data-driven materials design, we will survey a few in-house case studies and the application of accelerated learning showcasing rapid iteration between ideas, computations, insight and new materials development.
Speaker: Kristin Persson, UC Berkeley
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