论文标题
在ANI-1X数据集上学习使用均等变压器的小分子能量和原子质力
Learning Small Molecule Energies and Interatomic Forces with an Equivariant Transformer on the ANI-1x Dataset
论文作者
论文摘要
原子间能和力的准确预测对于高质量的分子动态模拟（MD）至关重要。机器学习算法可用于通过预测质量质量和力来克服经典MD的局限性。 SE（3） - 等级神经网络允许对空间关系进行推理，并利用旋转和翻译对称性。一种这样的算法是SE（3） - 转换器，我们适应ANI-1X数据集。我们的早期实验结果通过消融研究表明，更深层次的网络（具有额外的SE（3）转换层）可以达到必要的准确性，以允许与MD有效整合。但是，将需要更快的SE（3）转换器实现，例如Milesi最近发布的加速版本。
Accurate predictions of interatomic energies and forces are essential for high quality molecular dynamic simulations (MD). Machine learning algorithms can be used to overcome limitations of classical MD by predicting ab initio quality energies and forces. SE(3)-equivariant neural network allow reasoning over spatial relationships and exploiting the rotational and translational symmetries. One such algorithm is the SE(3)-Transformer, which we adapt for the ANI-1x dataset. Our early experimental results indicate through ablation studies that deeper networks - with additional SE(3)-Transformer layers - could reach necessary accuracies to allow effective integration with MD. However, faster implementations of the SE(3)-Transformer will be required, such as the recently published accelerated version by Milesi.
