AI RESEARCH
Universal and efficient graph neural networks with dynamic attention for machine learning interatomic potentials
arXiv CS.LG
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ArXi:2603.22810v1 Announce Type: new The core of molecular dynamics simulation fundamentally lies in the interatomic potential. Traditional empirical potentials lack accuracy, while first-principles methods are computationally prohibitive. Machine learning interatomic potentials (MLIPs) promise near-quantum accuracy at linear cost, but existing models still face challenges in efficiency and stability. We presents Machine Learning Advances Neural Network (MLANet), an efficient and robust graph neural network framework. MLANet.