Researchers at IISc developed ML-based methods to predict material properties

Researchers at the Indian Institute of Science (IISc), with collaborators at University College London, have developed machine learning-based methods to predict material properties even with limited data. This can aid in the discovery of materials with desired properties, such as semiconductors. The release by IISc states "In recent years, materials engineers have turned to machine learning models to predict which types of materials can possess specific properties such as electronic band gaps, formation energies, and mechanical properties, in order to design new materials. However, data on material properties – which is needed to train these models – is limited because testing materials is expensive and time consuming. This prompted researchers led by Sai Gautam Gopalakrishnan, Assistant Professor at the Department of Materials Engineering, IISc, to work on addressing this challenge. In a new study, they have found an efficient way to use a machine learning approach called transfer learning to predict the values of specific material properties." In transfer learning, a large model is first pre-trained on a large dataset and then fine-tuned to adapt to a smaller target dataset. “In this method, the model first learns to do a simple task like classifying images into, say, cats and non-cats, and is then trained for a specific task, like classifying images of tissues into tho...