10.6084/m9.figshare.11776395.v1
Srirangaraj Setlur
Srirangaraj
Setlur
Venu Govindaraju
Venu
Govindaraju
Krishna Rajan
Krishna
Rajan
Tom Furlani
Tom
Furlani
CSSI Element: MADE@UB: Materials Data Engineering at UB
figshare
2020
NSF-CSSI-2020-Poster
Artificial Intelligence
Chart data extraction
Machine Learning Framework
Materials Discovery
Materials Design
Artificial Intelligence and Image Processing
Information Retrieval and Web Search
Pattern Recognition and Data Mining
Metals and Alloy Materials
Materials Engineering not elsewhere classified
2020-01-31 16:25:35
Poster
https://figshare.com/articles/poster/CSSI_Element_MADE_UB_Materials_Data_Engineering_at_UB/11776395
<p>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.</p>