India stands at the forefront of leveraging artificial intelligence (AI) for scientific breakthroughs, with its researchers making remarkable strides in material discovery. Scientists at the Indian Institute of Science (IISc), in collaboration with University College London, have developed innovative AI models to predict material properties, even when data is scarce. This advancement can transform industries reliant on materials with specific properties, such as semiconductors, critical to India’s technological and economic aspirations.

The Challenge of Material Discovery

Material science underpins the development of technologies ranging from semiconductors to renewable energy solutions. However, the labour-intensive, expensive, and time-consuming nature of experimental testing often hinders discovering materials with desired properties. Machine learning (ML) offers a promising solution, enabling the prediction of material properties based on existing datasets.

Despite its potential, ML faces a significant challenge: the limited availability of high-quality data for training models. The IISc team, led by Assistant Professor Sai Gautam Gopalakrishnan, has turned to transfer learning to address this issue. This AI technique repurposes knowledge from large datasets to improve predictions for smaller, domain-specific datasets.

Breakthroughs in Predicting Material Properties

The IISc researchers utilized Graph Neural Networks (GNNs) to model the complex three-dimensional structures of materials. By optimizing their GNN architecture and fine-tuning the size of the training datasets, the team successfully predicted critical material properties such as:

• Dielectric constants: Essential for designing capacitors and insulators.
• Formation energies: Key to understanding material stability.
• Band gaps: Crucial for semiconductors used in electronics and solar cells.

Their approach demonstrated significant improvements over traditional models trained from scratch, highlighting the efficacy of transfer learning in material discovery.

Multi-Property Pre-Training

The team adopted a Multi-Property Pre-Training (MPT) framework to enhance the model's predictive capabilities further. This method allowed the simultaneous training of the model on multiple properties, enabling more comprehensive predictions. Remarkably, the model accurately predicted band gap values for two-dimensional materials not included in its training set, showcasing its potential to uncover new material properties in unexplored domains.

Real-World Applications and National Impact

The researchers are now applying their model to study ion movement within battery electrodes, a critical area for advancing energy storage technologies. This work aligns with India’s ambitions in the semiconductor manufacturing sector and its broader commitment to renewable energy and sustainable technologies.

The implications of these advancements extend beyond academia and industry:

• Boost to Semiconductor Manufacturing: Enhanced materials discovery could give India a competitive edge in the global semiconductor market, vital for electronics, defence, and telecommunication industries.
• Energy Innovations: Improved battery technologies could accelerate India's transition to clean energy, supporting initiatives like the National Electric Mobility Mission.
• Cost and Time Efficiency: AI-driven predictions significantly reduce the time and cost associated with experimental material testing.

Conclusion

India’s scientific community continues demonstrating how AI can drive innovation and solve pressing challenges. The work of IISc researchers exemplifies AI's role in fostering cross-disciplinary collaboration and addressing national priorities. By harnessing AI's potential, India is contributing to global advancements in material science and strengthening its position as a leader in technology-driven growth.

As the nation seeks to capitalize on this momentum, further investments in AI research and infrastructure will be critical. Initiatives like the IndiaAI Mission and partnerships between academic institutions and industries will play a pivotal role in shaping India’s future as a hub for AI-driven material discovery and innovation.

In the words of Assistant Professor Gopalakrishnan, “AI enables us to leverage existing knowledge for discoveries, accelerating the pace of innovation and expanding the boundaries of possibility.” India's journey is just beginning, and its potential is boundless.

Source: Article, India Today

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