Automated Analysis of Phase Diagrams

Kota, Bhargava Urala; Nair, Rathin Radhakrishnan; Setlur, Srirangaraj; Dasgupta, Aparajita; Broderick, Scott; Govindaraju, Venu; Rajan, Krishna

doi:10.1109/icdar.2017.256

Cited by 1 publication

(2 citation statements)

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“…As identified previously, a key component in predicting new metallic glasses has been to identify deep eutectic compositions indicating the importance of analyzing phase diagram information. We have previously demonstrated our automated phase diagram reading methodology 23 , which correctly classified phases and associate phase labels with 94% accuracy, and which we demonstrated with the capability to identify and characterize eutectic points. This capability is a key component of the design framework developed in this project.…”

Section: Resultsmentioning

confidence: 99%

“…To accelerate the interpretation of and design from phase diagrams, machine reading applications are utilized. We have previously described an approach for the rapid machine reading of phase diagrams 23 . We apply that approach to the assessment of phase diagrams, particularly as relates to deep eutectics, to allow for the rapid reading and integration of phase diagrams.…”

Section: Introductionmentioning

confidence: 99%

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Probabilistic Assessment of Glass Forming Ability Rules for Metallic Glasses Aided by Automated Analysis of Phase Diagrams

Dasgupta¹,

Broderick²,

Mack³

et al. 2019

Sci Rep

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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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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%