AI for Science is fragmented—siloed tools, incompatible formats, and workflows hindering scaling. We faced this in two radically different domains: neuroscience & chemistry. We show how PyTorch became the unifying backbone to build and open source NeuralSet and UMA—a toolkit and model for interoperability and scalability, accelerating discovery worldwide.
We explore three angles: 1) how NeuralSet aligns brain recordings and stimuli into one efficient configurable PyTorch DataLoader that keeps GPUs fed on terabyte-scale datasets to train large models linking brain activity to what people see and hear, 2) how UMA's architecture harnesses PyTorch to train on 500M+ atomic structures and deliver SOTA molecular simulations, and 3) the road to open sourcing AI for Science on PyTorch.
We distill cross-domain challenges and innovations: taming heterogeneous data at scale, designing pipelines with PyTorch primitives, ensuring reproducibility, and converting research into community artifacts. These apply to any team with complex multimodal data looking to open source on PyTorch. Attendees leave with concrete strategies to bridge research and production-quality open source scientific tooling.