New Delhi: How many of us have wished for mobile phone screens, glass utensils or window panes that resist damage? Glass makers also wish they had a mechanism for predicting glass compositions to develop products with tailored properties. IIT Delhi researchers have a solution.
Despite two thousand years of usage, developing glasses with tailored properties is still an open challenge and to address this problem, researchers at IIT Delhi have developed a first of its kind machine learning software — Python for Glass Genomics (PyGGi) — for predicting and optimising glass compositions.
PyGGi will allow researchers and companies to easily predict glasses with superior properties like scratch resistance and crack resistance at the tap of a button.
"Understanding and predicting the composition–structure–property relationship is the key to developing novel glasses such as bullet proof and scratch resistant glasses," said N M Anoop Krishnan, a professor at IIT Delhi who is one of the Project Investigators (PI).
"Data-driven approaches such as machine learning and artificial intelligence can exploit our existing knowledge to predict glasses for tailored applications. PyGGi is a software package developed using python, for predicting and optimising the properties of inorganic glasses," he added.
The main aim of PyGGi is to reduce the cost in predicting new glasses for tailored applications.
"PyGGi will be constantly updated and upgraded to meet the industrial and academic challenges in the field of glass science. We are also open to developing raw modules based on user requirements. These modules can be exclusively given to users who support the research in PyGGi," said professor Hariprasad Kodamana.
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