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physics.chem-ph2026
Graph Symbolic Distillation: Decoding Graph Data into Interpretable Formulas across Materials and Chemistry
Yuxuan Yao, Harald Oberhofer, Karsten Reuter +1
Graph data provide a natural representation of the interconnected structures and interactions underlying many chemical and materials systems. Graph neural networks (GNNs) can learn…
physics.chem-ph2025★ 4 cited
Foundation Models for Atomistic Simulation of Chemistry and Materials
Eric C. -Y. Yuan, Yunsheng Liu, Junmin Chen +11
Given the power of large language and large vision models, it is of profound and fundamental interest to ask if a foundational model based on data and parameter scaling laws and pr…