Papers

• Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems (2026)
  Xiao Zhu et al.
  2.00
• Latent PDE mapping for efficient physics-informed learning across geometries with limited data (2026)
  Ingvild Askim Adde et al.
  2.00
• Insect-inspired modular architectures as inductive biases for reinforcement learning (2026)
  Anne E. Staples
  1.83
• Autonomous Discovery of the Ising Model's Critical Parameters with Reinforcement Learning (2026)
  Hai Man et al.
  1.67
• Techno-economic optimization of a heat-pipe microreactor, part II: multi-objective optimization analysis (2026)
  Paul Seurin et al.
  1.67
• Reinforcement Learning for Chemical Ordering in Alloy Nanoparticles (2025)
  Jonas Elsborg et al.
  1.56