Imagine using artificial intelligence to compare two seemingly unrelated creations: biological tissue and Beethoven’s “Symphony No. 9.” At first glance, it may seem that a living system and a musical masterpiece have no connection. However, a novel ai method developed by Markus J. Buehler, McAfee Professor of Engineering and professor of civil and environmental engineering and mechanical engineering at MIT, closes this gap and uncovers shared patterns of complexity and order.
“By combining generative ai with graph-based computational tools, this approach reveals completely new ideas, concepts and designs that were previously unimaginable. “We can accelerate scientific discoveries by teaching generative ai to make novel predictions about never-before-seen ideas, concepts, and designs,” says Buehler.
Open access research, recently published in Machine Learning: Science and technologydemonstrates an advanced ai method that integrates generative knowledge extraction, graph-based representation, and multimodal intelligent graphical reasoning.
The work uses graphs developed 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 diverse systems by focusing on objects and their interactions, rather than their specific content. 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 the relationships between these objects). Using this approach, Buehler was able to teach the ai model to reason systematically about complex scientific concepts and behaviors. The symbolic relationships introduced through morphisms make it clear that ai does not simply draw analogies, but engages in deeper reasoning that maps abstract structures in different domains.
Buehler used this new method to analyze a collection of 1,000 scientific articles on biological materials and converted them into a knowledge map in the form of a graph. The graphic revealed how different pieces of information connect and was able to find clusters of related ideas and key points that link many concepts.
“What's really interesting is that the graph follows a scale-free nature, is highly connected, and can be used effectively for graphical reasoning,” says Buehler. “In other words, we teach ai systems to think about graph-based data to help them build better models of representations of the world and improve the ability to think and explore new ideas to enable discovery.”
Researchers can use this framework to answer complex questions, find gaps in current knowledge, suggest new material designs, predict how materials might behave, and link concepts that have never been connected before.
The ai model found unexpected similarities between the biological materials and “Symphony No. 9,” suggesting that both follow patterns of complexity. “Similar to how cells in biological materials interact in complex but organized ways to perform a function, Beethoven's Ninth Symphony organizes notes and musical themes to create a complex but coherent musical experience,” says Buehler.
In another experiment, the graph-based ai model recommended the creation of a new biological material inspired by the abstract patterns found in Wassily Kandinsky's painting, “Composition VII.” The ai suggested a new composite material based on mycelium. “The result of this material combines an innovative set of concepts that include a balance between chaos and order, tunable properties, porosity, mechanical strength and chemical functionality with complex patterns,” says Buehler. Inspired by an abstract painting, the ai created a material that balances being strong and functional, while also being adaptable and capable of performing different functions. The application could lead to the development of sustainable and innovative building materials, biodegradable alternatives to plastics, wearable technology and even biomedical devices.
With this advanced ai model, scientists can draw insights from music, art, and technology to analyze data from these fields and identify hidden patterns that could unlock a world of innovative possibilities for materials design, research, and even technology. music or visual art.
“Graph-based generative ai achieves a much greater degree of novelty, capability exploration, and technical detail than conventional approaches, and establishes a very useful framework for innovation by revealing hidden connections,” says Buehler. “This study not only contributes to the field of bioinspired materials and mechanics, but also lays the foundation for a future in which interdisciplinary research driven by ai and knowledge graphs can become a scientific and philosophical research tool.” as we look toward other future work.”
