DMREF: Collaborative Research: The Synthesis Genome: Data Mining for Synthesis of New Materials

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NON-TECHNICAL:Development of new materials is the key to addressing many of the technical challenges our society faces from energy storage to water treatment and purification. To offer just a few examples: in the oil industry, new materials are needed to withstand aggressive conditions, where failure comes with tremendous cost; electrified vehicle drive trains will be advanced by higher performing battery electrodes; carbon dioxide capture requires inexpensive new materials with the proper thermodynamic and kinetic behavior towards absorption and release. The rapid design of novel materials has been transformed by approaches where properties for many tens of thousands of materials can be predicted or inferred by a computer. The pace of commercially-realized advanced materials seems now to be limited by trial-and-error synthesis techniques. In other words, researchers have accelerated the process of knowing what to make such that the bottleneck is now how to make the structures. This research will learn from existing knowledge to develop insight on the synthesis of inorganic compounds. The analytical foundation of these activities stems from advances in machine learning that has allowed computers to excel in typically "human" tasks such as health care diagnoses and game show participation. This research will further accelerate the goals of efforts such as the Materials Genome Initiative for Global Competitiveness by enabling efficient synthesis of novel materials thereby speeding up evaluation of newly suggested materials.TECHNICAL:Materials are a key bottleneck in many technological advances such as efficient catalysis, clean energy generation, and water filtration. Materials Genome Initiative-style efforts have produced several examples of computationally designed materials in the fields of energy storage, catalysis, thermoelectrics, and hydrogen storage, as well as large data resources that can be used to screen for potentially transformative compounds. These successes in accelerated materials design have moved the bottleneck in materials development towards the synthesis of novel compounds, and much of the momentum and efficiency gained in the design process becomes gated by trial-and-error synthesis techniques. This research will do for solid state advanced materials synthesis what modern computational methods are doing for materials properties: Build predictive tools for synthesis so that targeted compounds can be synthesized more rapidly. This work will combine knowledge regarding synthesis, first principles modeling, and data mining to suggest synthesis routes for novel compounds.