论文标题
TORCHMD-NET：基于神经网络的分子电位的模棱两可的变压器
TorchMD-NET: Equivariant Transformers for Neural Network based Molecular Potentials
论文作者
论文摘要
量子机械性能的预测在历史上受到准确性和速度之间的权衡而困扰。机器学习潜力以前在该领域表现出了巨大的成功，在保持与经典力场相当的计算效率的同时达到了越来越高的准确性。在这项工作中，我们提出了Torchmd-net，这是一种新型的型号变压器（ET）体系结构，在准确性和计算效率方面都超过了MD17，ANI-1的最先进以及许多QM9目标。通过广泛的注意力分析，我们获得了对黑匣子预测变量的有价值的见解，并显示了从分子动力学或正常模式中采样的构象代表与构象的差异。此外，我们强调了数据集的重要性，包括评估分子电位的均衡构象。
The prediction of quantum mechanical properties is historically plagued by a trade-off between accuracy and speed. Machine learning potentials have previously shown great success in this domain, reaching increasingly better accuracy while maintaining computational efficiency comparable with classical force fields. In this work we propose TorchMD-NET, a novel equivariant transformer (ET) architecture, outperforming state-of-the-art on MD17, ANI-1, and many QM9 targets in both accuracy and computational efficiency. Through an extensive attention weight analysis, we gain valuable insights into the black box predictor and show differences in the learned representation of conformers versus conformations sampled from molecular dynamics or normal modes. Furthermore, we highlight the importance of datasets including off-equilibrium conformations for the evaluation of molecular potentials.
