Scheduled presentations:

• Machine‑Learning Interatomic Potentials Unveil the Fundamental Mechanisms of Hydrogen Embrittlement
  *S. Ogata (The University of Osaka)
• Predicting Morphology and Hydrogen Coverage of Ru Nanoparticle via Multiscale Modelling
  W. Xuan (Department of Engineering and System Science), Y. Liu (Department of Engineering and System Science), *H. Chen (Department of Engineering and System Science)
• KAN-EAM and KAN-SNAP Potentials for Five-Element High-Entropy Alloys Trained from VASP Trajectories
  *H. Huang (Academia Sinica), C. Pao (Academia Sinica)
• A Resource-Efficient Hybrid Quantum–Classical Force Fields for Large-Scale Molecular Dynamics
  *H. Cheng (RCAS, Academia Sinica), C. Pao (RCAS, Academia Sinica)
• Modeling Grain Evolution by Mechanics-based Graph Neural Cellular Automata
  *W. Lee (National Tsing Hua University), Y. Chou (National Tsing Hua University), Y. Guo (National Tsing Hua University), C. Lee (Academia Sinica), C. Pao (Academia Sinica), T. Huang (National Taiwan University)
• Reverse Design of Bio-inspired Layered Structures Combining Deep Learning and Reinforcement Learning
  *H. Mao (National Cheng Kung University), C. Yu (National Cheng Kung University)