Learning from an Informatics Approach Applied to Materials Design

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Nanomaterials will significantly impact commercial and military applications when context and understanding of their vast design and property space is achieved. Identifying the design parameters of important materials is one of the first steps in a materials design workflow that can optimize system-specific performance properties. Lockheed Martin has designed an informatics system, Nanotechnology Materials Data Mining, Modeling & Management (NMD-M3), to capture and guide nanotechnology-focused experimentation. This modular system is built upon open software standards supporting multiple experimental configurations. Predictions from this tool, with altered values for the input parameters, can assist in experimental design by quickly estimating modified system performance (including estimates of local accuracy and precision) without the need for extensive experimentation. Taking this idea a step further, inverse design through forward DoE prediction is included as part of the NMD-M3 system. A list of system configurations that meet or exceed specified performance goals are generated and then used to facilitate next-round experimental design. In this manner, tight integration of the NMD-M3 system with experimental confirmation leverages latent experimental knowledge and expedites goal achievement.

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Journal: TechConnect Briefs
Volume: 2, Nanotechnology 2013: Electronics, Devices, Fabrication, MEMS, Fluidics and Computational (Volume 2)
Published: May 12, 2013
Pages: 642 - 645
Industry sector: Advanced Materials & Manufacturing
Topicss: Advanced Manufacturing, Informatics, Modeling & Simulation
ISBN: 978-1-4822-0584-8