CSSI-Slide-OAC-1640867.pdf (377.61 kB)

CSSI Element: MADE@UB: Materials Data Engineering at UB

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posted on 31.01.2020 by Venu Govindaraju, Krishna Rajan, Srirangaraj Setlur, Tom Furlani
The primary outcomes of this project are: (i) Creation of AI tools to make valuable experimental data hitherto inaccessible for analysis, available to materials scientists - these tools are domain agnostic and facilitate extraction of data from information-rich sources such as scientific charts and diagrams in academic papers; examples include automatically extracting eutectic points from phase diagrams which was used to identify potential metallic glass forming compounds. (ii) Creation of a machine learning framework for materials scientists to accelerate the discovery of advanced materials. This framework contains synergistic building blocks that enable scientists to gather, model and visualize data. An easy-to-use graphical interface has also been developed to apply different state-of-the-art machine learning models on existing materials data. Performance comparison of different models as well as descriptors in terms of predicted properties is supported by the interface along with visualization of data and results to better understand physical phenomena. (iii) Creation of an alloy design foundry with tools to capture chemistry-processing-performance relationships hidden in the large amounts of legacy information, selecting most promising materials through consideration of chemistry-structure-performance trade-offs in sparse and uncertain data, and for exploration of connectivity of data through topological data analysis.


NSF OAC 1640867