Machine learning models from diverse modalities and architectures are being trained to predict molecular, material, and protein behaviors, yet it's unclear if they develop similar internal representations of matter. Research shows that nearly sixty scientific models, including string-, graph-, 3D atomistic, and protein-based modalities, exhibit highly aligned representations across various chemical systems. Despite different training datasets, models converge in representation space as they improve, suggesting a common underlying representation of physical reality. However, when faced with unfamiliar inputs, models tend to collapse into low-information states, indicating current limitations in training data and inductive biases. This research highlights representational alignment as a benchmark for evaluating the generality of scientific models, with implications for tracking universal representations and improving model transferability across scientific tasks. Understanding the convergence of representations in scientific models is crucial for developing reliable foundation models that generalize beyond their training data.
