How Google DeepMind’s AI breakthrough could revolutionise chip, battery development
- December 9, 2023
- Posted by: OptimizeIAS Team
- Category: DPN Topics
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How Google DeepMind’s AI breakthrough could revolutionise chip, battery development
Subject : Science and Tech
Section: Awareness in IT, Computer
More about the news:
- Researchers at Google DeepMind have made a significant breakthrough by using artificial intelligence (AI) to predict the structures of over 2 million new materials.
- This development, facilitated by the AI tool named Graph Networks for Materials Exploration (GNoME), holds immense potential in various sectors, including renewable energy, battery research, semiconductor design, and computing efficiency.
- While earlier claims of breakthroughs in materials like LK-99 faced scrutiny, the DeepMind AI tool offers a promising avenue for the design and generation of potential recipes for new materials, marking a notable advancement in the field.
Why is this significant
- The AI breakthrough by Google DeepMind has significantly increased the number of known ‘stable materials’ by ten-fold, encompassing inorganic crystals crucial for various modern tech applications such as computer chips and batteries.
- This development is particularly impactful in fields like the search for stable solid electrolytes to replace current Li-ion battery electrolytes and the exploration of new layered compounds akin to graphene for potential advancements in electronics and superconductors.
- DeepMind’s AI-led discovery employs filters to scale up the process, narrowing down a list of synthesizable materials that could meet specific requirements and potentially offering insights at the atomic bond level.
- Traditional methods of discovering stable materials involve time-consuming trial and error processes, making AI predictions a more efficient and groundbreaking approach.
How does GNoME actually work
- Google DeepMind’s project, Graph Networks for Materials Exploration (GNoME), utilizes a state-of-the-art graph neural network model (GNN) to predict the structures of over 2 million new materials.
- The model, trained using active learning, leverages a graph representation resembling atomic connections, making it well-suited for discovering new materials by identifying patterns not present in the original dataset.
- GNoME employs two pipelines—a structural one creating candidates akin to known crystals and a compositional one following a more randomized approach based on chemical formulas.
- The precision rate for predicting materials stability has been significantly boosted from 50% to around 80%, equivalent to nearly 800 years of knowledge based on publicly available stable predictions.
- GNoME was trained on crystal structure data from The Materials Project, contributing to the ongoing quest for innovative materials in various scientific domains.