Knowledge-Centric AI for Scientific Discovery

Recent breakthroughs in artificial intelligence have been driven primarily by data-centric approaches, including deep learning and large language models. However, despite their remarkable success, relying solely on purely data-driven methods has intrinsic limitations for scientific discovery. In this essay, I argue for knowledge-centric AI, reasoning grounded in scientific principles, domain structure, and constraints while learning from data. In our research at Cornell University and through interdisciplinary collaborations with researchers across institutions, this agenda has been shaped by data-limited scientific challenges, which has led us to develop knowledge-centric approaches, such as deep reasoning networks, which build interpretable, domain-aligned latent spaces and enforce differentiable constraints end-to-end. Knowledge-centric AI has enabled scientific advances, from automated crystal-structure phase mapping that uncovered high-performing alloyed mixtures to joint species-distribution models that inform conservation, while also delivering general AI methodologies. By aligning with the scientific method and integrating reasoning, learning, and principled experimentation, knowledge-centric AI can catalyze scientific innovation. In our work, this perspective is grounded in computational challenges central to a sustainable future.