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.

翻译：对量子机械特性的预测历来受到精确度和速度之间的权衡的困扰。 机器学习潜力以前在这一领域已经显示出巨大的成功,在保持与古典力场可比的计算效率的同时,更加精确。 在这项工作中,我们提出了TirchMD-NET(TRCHMD-NET)新颖的等同变压器(ET)结构、优于MD17、ANI-1(ANI-1)最新工艺以及许多QM9(QM9)目标的精确度和计算效率。通过广泛的关注重量分析,我们获得了对黑盒预测器的宝贵洞察力,并显示了在从分子动态或正常模式中提取的校准体与校准体之间的不同。此外,我们强调数据集的重要性,包括分子潜力评估的离平衡性一致性。