To reach this goal, we are bringing together a team of pure machine learning researchers with domain scientists, covering a wide variety of disciplines. In addition, we are guided by a scientific advisory group of world leading experts.

There is much preliminary research required to build a true foundation model for science. We are concentrating our efforts on the fundamentals of this space, and have thus far published research on key architectural components, from adapting language models for numerical data[1] to demonstrating transferability of surrogate models trained on diverse physical systems[2] to learning shared embeddings for multi-modal scientific data[3].