IIT Delhi researchers develop first of its kind machine learning software - Python for Glass Genomics (PyGGi) for predicting and optimizing glass compositions

IIT Delhi researchers develop first of its kind machine learning software - Python for Glass Genomics (PyGGi) for predicting and optimizing glass compositions

To address the issue of developing glasses with tailored properties, researchers at IIT Delhi have developed a first of its kind machine learning software -- Python for Glass Genomics (PyGGi) for predicting and optimizing glass compositions. The machine learning software PyGGi was launched on August 2, 2019 at IIT Delhi during an event on "Developments in Machine Learning for Material Informatics".

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.

Prof N. M. Anoop Krishnan (Civil Engg Dept, IIT Delhi), one of the Project Investigators (PI) said, “Understanding and predicting the composition–structure–property relationship is the key to developing novel glasses such as bullet proof and scratch resistant glasses. 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 optimizing the properties of inorganic glasses.”

The main aim of PyGGi is to reduce the cost in predicting new glasses for tailored applications. This is going to make glass development cheap and affordable.

Prof Hariprasad Kodamana (Chemical Engg Dept, IIT Delhi) who is the Co-PI of the project said, “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.”

The team is working on new capabilities customised to customer requirements and industrial needs. The researchers are also trying to extend this scalable approach to other materials as well with aim of accelerating materials discovery for healthcare, energy, and automotive applications.

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