Imagine using artificial intelligence to compare two seemingly unrelated creations—biological tissue and Beethoven’s “Symphony No. 9.” At first glance, a live system and a musical masterpiece may seem unrelated. However, a new artificial intelligence method developed by Markus J. Buehler, McAfee Professor of Engineering and MIT Professor of Civil and Environmental Engineering and Mechanical Engineering, bridges this gap and reveals shared patterns of complexity and order.
“By merging generative artificial intelligence with graph-based computational tools, this approach uncovers entirely new ideas, concepts and designs that were previously unimaginable. We can accelerate scientific discovery by teaching generative artificial intelligence to make new predictions about never-before-seen ideas, concepts and propositions,” says Buehler.
Open access research, recently published in Machine Learning: Science and Technologydemonstrates an advanced artificial intelligence method that integrates generative knowledge extraction, graph representation, and multimodal intelligent graph reasoning.
The work uses graphs created using methods inspired by category theory as a central mechanism to teach the model to understand symbolic relationships in science. Category theory, a branch of mathematics that deals with abstract structures and the relationships between them, provides a framework for understanding and unifying different systems by focusing on objects and their interactions, rather than their specific contents. In category theory, systems are viewed in terms of objects (which can be anything from numbers to more abstract entities such as structures or processes) and morphisms (arrows or functions that define relationships between these objects). Using this approach, Buehler was able to teach the AI model to think systematically about complex scientific concepts and behaviors. The symbolic relationships introduced through morphisms clearly show that AI does not just draw analogies, but engages in deeper reasoning that maps abstract structures across different domains.
Buehler used this new method to analyze a collection of 1,000 scientific papers on biological materials and convert them into a knowledge map in the form of a graph. The graph revealed how different pieces of information were connected and was able to find groups of related ideas and key points that tied many concepts together.
“What’s really interesting is that the graph is scale-free, highly connected, and can be effectively used for graph reasoning,” says Buehler. “In other words, we’re teaching AI systems to think about graph-based data to help them create better models of the world’s representation and improve their ability to think and explore new ideas to enable discovery.”
Researchers can use this framework to answer complex questions, find gaps in current knowledge, propose new material designs and predict how materials might behave, and connect concepts that have never been connected before.
The AI model found unexpected similarities between biological materials and “Symphony No. 9,” suggesting that both follow patterns of complexity. “Just as cells in biological materials interact in a complex but organized way to perform a function, Beethoven’s 9th Symphony arranges musical notes and themes to create a complex but coherent musical experience,” says Buehler.
In another experiment, the AI model on the graph recommended creating a new biological material inspired by the abstract patterns found in Wassila Kandinsky’s painting “Composition VII.” AI designed a new mycelium-based composite material. “The result of this material combines an innovative set of concepts that include a balance of chaos and order, tunable properties, porosity, mechanical strength, and complex chemical functionality,” notes Buehler. Drawing inspiration from abstract painting, AI has created a material that balances strength and functionality while being adaptable and able to fulfill different roles. The application could lead to the development of innovative sustainable building materials, biodegradable alternatives to plastics, wearable technology and even biomedical devices.
With this advanced AI model, researchers can draw insights from music, art, and technology and analyze data from these fields to identify hidden patterns that could spark a world of innovative possibilities for material design, research, and even music or visual art.
“Graph-based generative AI achieves a much higher degree of novelty, explores capacity and technical details than conventional approaches, and creates a broadly useful framework for innovation by uncovering hidden connections,” says Buehler. “This study not only contributes to the field of bio-inspired materials and mechanics, but also paves the way for a future where interdisciplinary research powered by artificial intelligence and knowledge graphs can become a tool for scientific and philosophical inquiry as we look to further future work.” .”