Computational simulations play a central role in scientific discovery, and machine learning (ML) has emerged as a promising alternative to traditional physics-based modeling. However, scientific modeling requires physically meaningful predictions, raising a fundamental question for data-driven methods: to what extent can physical inductive biases - that is, prior assumptions about the structure of the physical world - emerge by learning from data alone? In atomistic modeling, for example, ML architectures have historically embedded strong physical inductive biases - such as geometric locality and graph structure - based on the assumption that these priors are necessary for physical predictions. We systematically develop an understanding of how physical patterns can alternatively be discovered directly from data by training a model without domain-specific priors, including any manually defined atomistic pairwise interactions. We find that the model autonomously recovers key physical structure, such as learned interatomic interaction strengths that mirror classical electrostatics and interaction cutoffs consistent with traditional physical models. We further demonstrate predictable neural scaling law behavior with increased data and compute, and find accuracy on certain metrics competitive with physics-informed architectures. Our results clarify the boundary between engineered inductive bias and learnable physical structure, suggesting that general-purpose architectures can serve as principled baselines for scientific modeling, with explicit priors introduced only when empirically necessary.