As AI+Science went more mainstream in 2025, our team’s seminal contributions to the field are getting wide recognition. While language models have been used by “AI-scientists” to generate new ideas, they do not solve the main bottleneck in scientific discovery. The cost and time needed for physical experimentation is the main limiting factor for trying out many new ideas. AI with physical understanding aims to replace expensive physical experimentation with digital exploration. In 2020, we invented Neural Operators to learn multiscale physical phenomena that laid the foundations for such Physical AI. In 2025, we deepened these foundations and also pushed the frontier in a wide range of scientific applications.

The California Institute of Technology and the University of Chicago are centers of gravity for the study, application, and use of AI and Machine Learning to enable scientific discovery across the physical and biological sciences, advancing core AI principles and training a new generation of interdisciplinary scientists. To both advance this scientific and technical pursuit and demonstrate the leadership of Caltech and UChicago in this space, we will host the The Caltech and University of Chicago Conference on AI+Science, Sponsored by the Margot and Tom Pritzker Foundation, at Caltech from November 10-11, 2025. This event will bring together an elite and diverse cohort of leading researchers in core AI and domain sciences to lead conversations and drive partnerships that will shape future inquiry, industry investment, and entrepreneurial opportunities.

How can we advance efforts to harness AI to deliver positive impacts to people's lives?

We construct a data set of metal–organic framework (MOF) linkers and employ a fine-tuned GPT assistant to propose MOF linker designs by mutating and modifying the existing linker structures. This strategy allows the GPT model to learn the intricate language of chemistry in molecular representations, thereby achieving an enhanced accuracy in generating linker structures compared with its base models. Aiming to highlight the significance of linker design strategies in advancing the discovery of water-harvesting MOFs, we conducted a systematic MOF variant expansion upon state-of-the-art MOF-303 utilizing a multidimensional approach that integrates linker extension with multivariate tuning strategies. We synthesized a series of isoreticular aluminum MOFs, termed Long-Arm MOFs (LAMOF-1 to LAMOF-10), featuring linkers that bear various combinations of heteroatoms in their five-membered ring moiety, replacing pyrazole with either thiophene, furan, or thiazole rings or a combination of two. Beyond their consistent and robust architecture, as demonstrated by permanent porosity and thermal stability, the LAMOF series offers a generalizable synthesis strategy. Importantly, these 10 LAMOFs establish new benchmarks for water uptake (up to 0.64 g g–1) and operational humidity ranges (between 13 and 53%), thereby expanding the diversity of water-harvesting MOFs.

Tracing the roots of neural networks, the building blocks of modern AI, at Caltech. By Whitney Clavin

Scientific problems require resolving multi-scale phenomena across different resolutions and learning solution operators in infinite-dimensional function spaces. Neural operators provide a powerful fr...

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Learn how Indian American professor Anima Anandkumar is revolutionizing the world of artificial intelligence to drive new scientific discoveries. Explore her cutting-edge research and innovative appro...

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