interatomic-potentials

NEMORA 是一种将 Fast Multipole Method 推广为可学习等变算子的新方法，用于在等变图神经网络中建模长程原子间相互作用，具备线性时间与内存复杂度，可处理数十万原子体系，并在长程基准测试上将力和能量误差降低一个至三个数量级。

This paper introduces a budget-dependent method that combines coverage-based and response-based training-set selection strategies for machine-learned interatomic potentials, aiming to improve accuracy and efficiency in materials simulations.

The paper provides a completeness theory proving that multi-layer message passing in graph neural networks achieves universal approximation for interatomic potentials, justifying common architectural practices in machine-learned potentials.

Researchers from MIT, University of Warwick, and NVIDIA introduce Stein Kernelized Molecular Dynamics (SKMD), an enhanced sampling method that uses interacting particle dynamics to acquire informative training configurations for active learning and fine-tuning of machine learning interatomic potentials (MLIPs). SKMD is a stochastic variant of Stein variational gradient descent adapted for molecular dynamics, preserving the Boltzmann distribution while achieving higher model accuracy in fewer training iterations compared to baselines.