The evolution of machine learning potentials (MLPs) has revolutionized computational chemistry, particularly in fields like molecular dynamics (MD) and quantum chemistry. Among these innovations, the ANI (Accurate Neural Network Interaction) family of potentials stands out for its exceptional accuracy and adaptability. This dissertation explores the advanced concepts, mathematical foundations, and real-world applications of MLPs, focusing … Continue reading Machine Learning Potentials in 2025: ANI Family, Differentiable Simulations, and Advanced Applications
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