For centuries, discovering new materials was a painstaking process of trial and error, often relying on serendipity. Today, artificial intelligence (AI) is ushering in a new era of materials science, acting as a powerful accelerator in the quest for novel substances with tailored properties. While AI’s role in drug design garners much attention, its impact on materials discovery is equally transformative yet often overlooked.
Traditional methods of materials discovery are slow and resource-intensive. Scientists synthesize and test compounds iteratively, a process that can take years, if not decades, to yield a breakthrough. AI, particularly machine learning, offers a way to circumvent this bottleneck. By analyzing vast datasets of existing materials and their properties, AI algorithms can identify patterns and relationships that are invisible to the human eye. These insights enable researchers to predict the properties of hypothetical materials, guiding the search for the most promising candidates.
One of the most exciting applications of AI in materials science is the discovery of new materials for energy technologies. For instance, the development of more efficient batteries and solar cells hinges on finding materials with specific electrochemical and optoelectronic properties. AI is being used to design novel electrolytes for batteries [1], discover new perovskite materials for solar cells [2], and even identify catalysts for hydrogen production [3]. These efforts have the potential to revolutionize energy storage and generation, paving the way for a sustainable future.
DeepMind’s recent GNoME (Graph Networks for Materials Exploration) project is a prime example of the power of AI in accelerating materials discovery. GNoME utilizes graph neural networks to predict the stability of crystal structures. In a groundbreaking study, GNoME discovered 2.2 million new inorganic crystals, including 380,000 that are predicted to be stable enough to be synthesized [4]. This is an order of magnitude increase in the number of known stable materials, effectively expanding the materials universe. These discoveries span a range of potential applications, from next-generation semiconductors to advanced ceramics.
The implications of GNoME and similar AI-driven initiatives are profound. By dramatically accelerating the pace of materials discovery, AI is shortening the time it takes to develop new technologies. This is crucial in addressing pressing global challenges like climate change and resource scarcity. Furthermore, AI enables the design of materials with bespoke properties that are tailored to specific applications. This level of control opens up exciting possibilities for creating more substantial, lighter, more durable, and more efficient materials than anything we have today.
While challenges remain, such as the need for high-quality datasets and the interpretability of AI models, the future of materials science is undoubtedly intertwined with artificial intelligence. As AI algorithms become more sophisticated and computational resources more powerful, we can expect even more rapid progress in this field, leading to a new era of materials innovation that will transform industries and improve lives.
Citations:
- [1] Sendek, A. D., et al. “Holistic computational structure screening of more than 12 000 candidates for solid lithium-ion conductor materials.” Energy 1 & Environmental Science 10.1 (2017): 306-320. 2
- [2] Jørgensen, M., et al. “Machine learning-accelerated design of functional organic materials.” Nature Reviews Materials 5.5 (2020): 379-393.
- [3] Tran, K., and Ulissi, Z. W. “Active learning across datasets improves accuracy and reduces data requirements for the prediction of catalyst properties.” Nature Catalysis 1.9 (2018): 696-703.
- [4] Merchant, A. et al. “Scaling deep learning for materials discovery”. Nature 624, pages 80–85 (2023).
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