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We present a study on automated analysis of phase diagrams to aid the materials science community that attempts to lay the groundwork for a large-scale, searchable, digitized database of phases of a wide variety of materials at different physical conditions and compositions. For this work, we concentrate on around 80 thermodynamic phase diagrams of binary metallic alloy systems which give phase information of alloys at varied temperatures and mixture ratios. We use image processing techniques to isolate phase boundaries and subsequently extract areas of the same phase. Simultaneously, document analysis techniques are employed to recognize and group the text used to label the phases; text present along the axes is identified so as to map image coordinates (x, y) to physical coordinates. Labels of unlabeled phases are inferred using standard rules. Once a phase diagram is thus digitized we are able to provide the phase of all materials present in our database at any given temperature and alloy mixture ratio. Using the digitized data, more complex queries may also be supported in the future. We evaluate our system by measuring the correctness of labeling of phase regions.
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Probabilistic Assessment of Glass Forming Ability Rules for Metallic Glasses Aided by Automated Analysis of Phase Diagrams
Abstract The use of machine learning techniques to expedite the discovery and development of new materials is an essential step towards the acceleration of a new generation of domain-specific highly functional material systems. In this paper, we use the test case of bulk metallic glasses to highlight the key issues in the field of high throughput predictions and propose a new probabilistic analysis of rules for glass forming ability using rough set theory. This approach has been applied to a broad range of binary alloy compositions in order to predict new metallic glass compositions. Our data driven approach takes into account not only a broad variety of thermodynamic, structural and kinetic based criteria, but also incorporates qualitative and descriptive attributes associated with eutectic points in phase diagrams. For the latter, we demonstrate the use of automated machine learning methods that go far beyond text recognition approaches by also being able to interpret phase diagrams. When combined with structural descriptors, this approach provides the foundations to develop a hierarchical probabilistic predication tool that can rank the feasibility of glass formation.
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- Award ID(s):
- 1640867
- PAR ID:
- 10153594
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- Scientific Reports
- Volume:
- 9
- Issue:
- 1
- ISSN:
- 2045-2322
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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