most citedGenerator of Neural Network Potential for Molecular Dynamics: Constructing Robust and Accurate Potentials with Active Learning for Nanosecond-scale Simulations

17 citations · 17 across the 4 of their papers we have counts for

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cs.LG2025

Knowledge Distillation Framework for Accelerating High-Accuracy Neural Network-Based Molecular Dynamics Simulations

Naoki Matsumura, Yuta Yoshimoto, Yuto Iwasaki +2

Neural network potentials (NNPs) offer a powerful alternative to traditional force fields for molecular dynamics (MD) simulations. Accurate and stable MD simulations, crucial for e…

cs.LG2025

LaMM: Semi-Supervised Pre-Training of Large-Scale Materials Models

Yosuke Oyama, Yusuke Majima, Eiji Ohta +1

Neural network potentials (NNPs) are crucial for accelerating computational materials science by surrogating density functional theory (DFT) calculations. Improving their accuracy…