Universally Converging Representations of Matter in Scientific Foundation Models (arxiv.org)

Recent research reveals that machine learning models, despite their diverse architectures and modalities, tend to learn highly aligned representations of matter, such as molecules, materials, and proteins. This study examined nearly sixty scientific models, including those based on string theory, graphs, 3D atomics, and proteins, confirming that models trained on varied datasets consistently produce similar representations, particularly for small molecules. This convergence suggests that these foundation models possess a common underlying understanding of physical reality, enabling them to generalize beyond their training data. The significance of this finding lies in its implications for the AI/ML community, as it provides a quantitative benchmark for assessing the generality of scientific models. The study highlights two performance regimes: models perform well and align closely when dealing with familiar inputs but struggle and collapse into low-information representations when encountering unfamiliar structures. This underscores current limitations in model training and inductive bias, indicating that while some progress has been made toward universal representations of matter, further advancements are necessary. Ultimately, these insights will help refine model selection and optimization, paving the way for more effective applications across various scientific domains.