Materials Science

Cornell’s ME-AI Model Captures Human Intuition for Quantum Materials Discovery Researchers at Cornell University have introduced a novel machine-learning model, Materials Expert-Artificial Intelligence (ME-AI), designed to quantify and encapsulate the intuition of human experts to accelerate the discovery of new quantum materials. The model effectively “bottles” expert knowledge into descriptors that can accurately predict a material’s functional properties. In a proof-of-concept test, ME-AI was trained on expert-curated data to identify a specific desirable characteristic across 879 materials. The model not only reproduced the human expert’s intuition but also expanded upon it, proving its ability to generalize predictions to other compound sets. This research establishes a powerful model for future collaborations between materials scientists and AI experts to push the boundaries of scientific discovery. ...

AI Discovers Stronger, More Durable Plastics in Materials Science Breakthrough Researchers at MIT and Duke University have utilized machine learning to develop significantly more durable plastics, a breakthrough that could lead to longer-lasting products and a reduction in plastic waste. The AI model identified novel crosslinker molecules, known as mechanophores, that can be integrated into polymer materials to make them more resistant to tearing. These mechanophores respond to mechanical force, allowing the material to withstand greater stress before breaking. After the AI model performed a rapid computational analysis of thousands of potential molecules, a task that would have been incredibly time-consuming with traditional methods, researchers synthesized a polymer with one of the identified candidates. The resulting material was found to be approximately four times tougher than plastics made with standard crosslinkers. This AI-driven approach to polymer science not only accelerated the discovery process but also uncovered new design principles for creating more resilient polymers. ...