Google Deepmind - New materials discovered with deep learning
Google DeepMind has developed a deep learning tool called "Graph Networks for Materials Exploration" (GNoME), which has discovered 2.2 million new crystals, including 380,000 stable materials. This is a significant breakthrough for the field of new material development, as it demonstrates the potential of AI in accelerating the discovery and development of new materials.
GNoME's primary function is to predict the stability of new materials, which is a critical factor in their potential for technological applications. Traditionally, discovering new, stable crystals has been a slow and laborious, often involving months of experimentation. However, the introduction of GNoME has significantly increased the speed and efficiency of this process.
Of the 2.2 million new crystals predicted by GNoME, 380,000 are the most stable, making them promising candidates for future technologies. Furthermore, 736 new structures have been independently created experimentally in labs worldwide.
This breakthrough is a testament to the power of AI in materials science and demonstrates the potential for AI to guide materials discovery, experimentation, and synthesis on a global scale. The discovery of 2.2 million new materials by GNoME represents an unprecedented scale and prediction accuracy, equivalent to about 800 years' worth of knowledge.
In collaboration with Google DeepMind, a team of researchers at the Lawrence Berkeley National Laboratory has published a paper showing how AI predictions can be leveraged for autonomous material synthesis. This collaboration further underscores the potential of AI in guiding materials discovery and development.
The use of AI and robotics in discovering and developing new materials is crucial for advancing various technologies, including clean-energy technologies and next-generation electronics. AI, particularly deep learning tools like GNoME, is revolutionizing new material development by predicting the stability of new materials, accelerating the discovery process, and opening up new possibilities for technological advancement.
Over time, the 380,000 new materials will be added to the Materials Project.
What is the significance for Nature-inspired Innovation?
The discovery of new materials using AI has significant implications for nature-inspired innovation. Google DeepMind's GNoME discovered 2.2 million new crystals relevant to nature-inspired innovation (biomimicry). Scientists and engineers look to nature's designs to create sustainable and efficient solutions.
Nature-inspired materials have specific functionalities that harness electrical, mechanical, biological, chemical, sustainable, or combined gains. The discovery of new materials, particularly stable crystals, can provide new insights into these functionalities and open new possibilities for nature-inspired innovation. The stable crystals discovered by GNoME can mimic natural structures or processes, leading to the development of new, efficient, and sustainable technologies.
Using AI in materials science can speed up the discovery and development of nature-inspired materials. By predicting the stability of new materials, AI tools like GNoME can help identify promising candidates for nature-inspired innovation more quickly and efficiently. This can significantly speed up the development of new technologies inspired by nature, from energy-efficient buildings to advanced medical devices.
Furthermore, the collaboration between Google DeepMind and researchers at the Lawrence Berkeley National Laboratory demonstrates the potential of AI to guide the discovery and development of materials on a global scale. This could lead to the discovery of new nature-inspired materials and the development of innovative, sustainable technologies.
In conclusion, using AI in materials science, particularly in discovering new materials, has significant potential for nature-inspired innovation. By accelerating the discovery process and providing new insights into material functionalities, AI can help drive the development of new, nature-inspired technologies that are more efficient and sustainable.
