MolField: Representing Molecules as Neural Fields via Hypernetworks

Molecular representations fundamentally shape how machine learning systems reason about molecular structure and physical properties. Most existing approaches adopt a discrete pipeline: molecules are encoded as sequences, graphs, or point clouds, mapped to fixed-dimensional embeddings, and then used for task-specific prediction. This paradigm treats molecules as discrete objects, despite their intrinsically continuous and field-like physical nature. We argue that molecular learning can instead be formulated as learning in function space. Specifically, we model each molecule as a continuous function over three-dimensional (3D) space and treat this molecular field as the primary object of representation. From this perspective, conventional molecular representations arise as particular sampling schemes of an underlying continuous object. We instantiate this formulation with MolField, a hyper-network-based framework that generates molecular fields. To ensure consistency under rigid motions, these functions are defined over canonicalized coordinates, yielding invariance to global SE(3) transformations. To enable learning directly over functions, we introduce a structured weight tokenization and train a sequence-based hyper-network that generates the parameters of each molecular field. We evaluate MolField on dynamic molecular surface modeling, molecular property prediction, and molecular generation. Our results show that treating molecules as neural fields fundamentally changes how molecular representations generalize and yields downstream behavior that is stable to how molecules are discretized or queried.