טרנספורמרים גילו את המבנה המולקולרי ללא ידע קודם

תקציר מקורי באנגליתarXiv:2510.02259v3 Announce Type: replace Abstract: 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 this work, we study atomistic modeling, a representative field in the computational sciences where ML architectures have historically embedded strong physical inductive biases-such as geometric locality and graph structure. We develop a systematic understanding of how physical patterns can