Sucheta Swetlana

About

I am working on development of machine learning models for the prediction of properties. My areas of interest are focused on design and optimisation of mechanical properties of alloys using data driven modelling. I have expertise on image processing tools, python and machine learning. I also work on atomistic simulation of materials using first principles.

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Publications (6)

• Chemistry and Local Environment Adaptive Representation graphs as material descriptors
  Acta Materialia 2024 DOI: 10.1016/j.actamat.2024.120122
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• Shape Change of Cu Thin Films on Nonreacting Metal Substrates and the Effects of Oxidation during Vacuum Annealing
  The Journal of Physical Chemistry C 2023 DOI: 10.1021/acs.jpcc.3c05626
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• Machine learning assisted interpretation of creep and fatigue life in titanium alloys
  APL Machine Learning 2023 DOI: 10.1063/5.0129037
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• An Interpretable Machine Learning Approach for Predicting Creep and Fatigue Life in Ti-Alloys
  2022 DOI: 10.2139/ssrn.4090163
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• Accelerated prediction of Vickers hardness of Co- and Ni-based superalloys from microstructure and composition using advanced image processing techniques and machine learning
  Acta Materialia 2020 DOI: 10.1016/j.actamat.2020.06.042
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• Development of Vickers hardness prediction models via microstructural analysis and machine learning
  Journal of Materials Science 2020 DOI: 10.1007/s10853-020-05153-w
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