An AImethod developed by Professor Markus Buehler can discover hidden connections between science and art to recommend new materials. Imagine using AI to compare two seemingly unrelated creations—biological tissues and Beethoven’s Ninth Symphony. At first glance, living systems and musical masterpieces seem unrelated. However, a novel AI method developed by Markus J. Buehler, MIT’s McAfee Professor of Engineering, professor of civil and environmental engineering, and professor of mechanical engineering, bridges that gap, revealing common patterns of complexity and order.
“By combining generative AIwith graph-based computational tools, this approach reveals entirely new ideas, concepts, and designs that were previously unimaginable. We can accelerate scientific discovery by teaching generative AI to make novel predictions about ideas, concepts, and designs that have never been seen before,” Buehler says.
The open-access study, recently published in Machine Learning: Science & Technology, demonstrates an advanced AImethod that integrates generative knowledge extraction, graph-based representations, and multimodal intelligent graph reasoning.
The research used graphs developed using methods inspired by category theory as a core mechanism to teach the model to understand symbolic relationships in science. Category theory is a branch of mathematics that studies abstract structures and the relationships between them. It provides a framework for understanding and unifying different systems by focusing on objects and their interactions rather than their specific content. In category theory, systems are viewed as objects (which can be anything from numbers to more abstract entities such as structures or processes) and morphisms (arrows or functions that define the relationships between these objects). By using this approach, Buehler was able to teach theAI model to systematically reason about complex scientific concepts and behaviors. The symbolic relationships introduced through morphisms made it clear that the AI was not just making analogies, but was also engaging in deeper reasoning, mapping abstract structures to different domains.
Using this new approach, Buehler analyzed 1,000 scientific papers on biomaterials and transformed them into a knowledge graph in the form of a graph. The graph revealed connections between different information and was able to find relevant ideas and key points that tie many concepts together.
“What’s really interesting is that the graph follows the scale-free property and is highly connected, which can be effectively used for graph reasoning,” Buehler said. “In other words, we teach AIsystems to think about graph-based data to help them build better representations of the world and enhance their ability to think about and explore new ideas to enable discovery.”
In another experiment, a graph-based AImodel suggested making a new biomaterial inspired by the abstract patterns in Wassily Kandinsky’s painting Composition VII. The AI suggested making a new mycelium-based composite material. “This material combines a range of innovative concepts, including a balance of chaos and order, tunable properties, porosity, mechanical strength, and chemical functionality in a complex pattern,” Buehler noted. By drawing inspiration from abstract paintings, AI created a material that balances strength and functionality while being adaptable and able to perform different roles. This application could facilitate the development of innovative sustainable building materials, biodegradable plastic alternatives, wearable technology, and even biomedical devices.
An AImethod developed by Professor Markus Buehler can discover hidden connections between science and art to recommend new materials. Imagine using AI to compare two seemingly unrelated creations—biological tissues and Beethoven’s Ninth Symphony. At first glance, living systems and musical masterpieces seem unrelated. However, a novel AI method developed by Markus J. Buehler, MIT’s McAfee Professor of Engineering, professor of civil and environmental engineering, and professor of mechanical engineering, bridges that gap, revealing common patterns of complexity and order.
The research used graphs developed using methods inspired by category theory as a core mechanism to teach the model to understand symbolic relationships in science. Category theory is a branch of mathematics that studies abstract structures and the relationships between them. It provides a framework for understanding and unifying different systems by focusing on objects and their interactions rather than their specific content. In category theory, systems are viewed as objects (which can be anything from numbers to more abstract entities such as structures or processes) and morphisms (arrows or functions that define the relationships between these objects). By using this approach, Buehler was able to teach theAI model to systematically reason about complex scientific concepts and behaviors. The symbolic relationships introduced through morphisms made it clear that the AI was not just making analogies, but was also engaging in deeper reasoning, mapping abstract structures to different domains.
Using this new approach, Buehler analyzed 1,000 scientific papers on biomaterials and transformed them into a knowledge graph in the form of a graph. The graph revealed connections between different information and was able to find relevant ideas and key points that tie many concepts together.
“What’s really interesting is that the graph follows the scale-free property and is highly connected, which can be effectively used for graph reasoning,” Buehler said. “In other words, we teach AIsystems to think about graph-based data to help them build better representations of the world and enhance their ability to think about and explore new ideas to enable discovery.”
In another experiment, a graph-based AImodel suggested making a new biomaterial inspired by the abstract patterns in Wassily Kandinsky’s painting Composition VII. The AI suggested making a new mycelium-based composite material. “This material combines a range of innovative concepts, including a balance of chaos and order, tunable properties, porosity, mechanical strength, and chemical functionality in a complex pattern,” Buehler noted. By drawing inspiration from abstract paintings, AI created a material that balances strength and functionality while being adaptable and able to perform different roles. This application could facilitate the development of innovative sustainable building materials, biodegradable plastic alternatives, wearable technology, and even biomedical devices.
“Graph-based generative AI is more innovative, exploratory, and technically detailed than traditional methods, and establishes a broadly useful innovation framework by revealing hidden connections,” said Buehler. “This research not only contributes to the field of biomimetic materials and mechanics, but also lays the foundation for future interdisciplinary research driven by AI and knowledge graphs to become a tool for scientific and philosophical inquiry. We look forward to more research results in the future.