The accuracy of any machine learning potential can only be as good as the data used in the fitting process. The most efficient model therefore selects the training data that will yield the highest accuracy compared to the cost of obtaining the training data. We investigate the convergence of prediction errors of quantum machine learning models for organic molecules trained on energy and force labels, two common data types in molecular simulations. When training models for the potential energy surface of a single molecule, we find that the inclusion of atomic forces in the training data increases the accuracy of the predicted energies and forces 7-fold, compared to models trained on energy only. Surprisingly, for models trained on sets of organic molecules of varying size and composition in non-equilibrium conformations, inclusion of forces in the training does not improve the predicted energies of unseen molecules in new conformations. Predicted forces, however, improve about 7-...

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关于梯度在分子能量和力的机器学习中的作用

任何具有机器学习潜力的准确性都只能与拟合过程中使用的数据一样好。因此，最有效的模型选择的训练数据与获得训练数据的成本相比将产生最高的准确性。我们研究了在能量和力标签上训练的有机分子的量子机器学习模型的预测误差的收敛性，这是分子模拟中的两种常见数据类型。当训练单个分子的势能表面的模型时，我们发现与仅通过能量训练的模型相比，在训练数据中包含原子力会使预测的能量和力的准确性提高7倍。出乎意料的是，对于在非平衡构象中大小和组成各异的有机分子集上训练的模型，在训练中包含力量并不能提高新构象中看不见的分子的预测能量。但是，预计的力量会提高约7 -...