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README.md

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 ### Abstract:
 Alloy Property Prediction is a task under the sub field of Alloy Material Science wherein Machine Learning has been applied rigorously.  This is modelled as a Supervised Task wherein AlloyComposition is provided for the Model to predict a desired property. Efficiency of tasks such as AlloyProperty Prediction, Alloy Synthesis can be modelled additionally with an Unsupervised Pre-trainingTask. We describe the idea of Pre-training using Language Modelling kind of approach interms of Alloy Compositions.We specifically inspect that random masking proposed in is not suitable for modelling Alloys. We further go on proposing two types of masking strategies that are used to train GlassBERTa to encompass the properties of an Alloy Composition. The results suggest that Pre-training is an important field of direction in this field of research for further improvement.
 ### Authors:
-Reshinth Adithyan, Aditya TS, Roakesh, Jothikrishna, Kalaiselvan
+Reshinth Adithyan, Aditya TS, Roakesh, Jothikrishna, Kalaiselvan Baskaran
+### Footnote:
+Work done under [MLDMM Lab](https://sites.google.com/view/mldmm-lab/home)
+![alt text](https://lh4.googleusercontent.com/4L1C4_7ZBScAs9TIlkbyfjlotpnlnA4w22PLJXDWrYzh434Cu8RBhExvfBNdV8roOSb_k3WsM6MQHxv0zErcUhg=w16383 "Machine Learning for Design of Mechanical Materials Lab")