IISc researchers develop new ML model to find semiconductor properties
material properties with limited data. The research shows that this approach can aid in the discovery of semiconductors. It can also predict how quickly ions can move within electrodes in a battery, helping to build better energy storage devices.
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The research team, led by Sai Gautam Gopalakrishnan, Assistant Professor at the Department of Materials Engineering, developed a model based on Graph Neural Networks (GNNs) for the study.
IISc, in a release, explained that the lack of data on material properties—which is needed to train models that can predict which types of materials possess specific properties, such as electronic band gaps, formation energies, and mechanical properties—is a hindrance. This is due to expensive and time-consuming methods currently in use.
In transfer learning, researchers use a large model 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 those containing tumors and those not containing tumors for cancer diagnosis,” Gopalakrishnan explained.
“The architecture of the GNN, such as the
