AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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A scalable ML-assisted paradigm for realistic electrocatalyst screening is established and design principles for the discovery of next-generation dual-atom ORR catalysts are provided.
This work presents a physics-informed pretraining strategy that leverages simple empirical potentials to improve the robustness and stability of MLIPs for MD simulations and demonstrates that this physics-informed pretraining consistently improves both prediction accuracy as well as stability in MD.
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