Papers

• How Far Can You Grow? Characterizing the Extrapolation Frontier of Graph Generative Models for Materials Science (2026)
  Can Polat et al.
  1.67
• A Cross-Domain Graph Learning Protocol for Single-Step Molecular Geometry Refinement (2026)
  Chengchun Liu et al.
  1.61
• Multi-task parallelism for robust pre-training of graph foundation models on multi-source, multi-fidelity atomistic modeling data (2025)
  Massimiliano Lupo Pasini and Jong Youl Choi and Pei Zhang and Kshitij Mehta and Rylie Weaver and Ashwin M. Aji and Karl W. Schulz and Jorda Polo and Prasanna Balaprakash
  1.22
• Encoding protein dynamic information in graph representation for functional residue identification (2021)
  Yuan Chiang et al.
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• Prediction of the electron density of states for crystalline compounds with Atomistic Line Graph Neural Networks (ALIGNN) (2022)
  Prathik R Kaundinya et al.
  —