论文标题
完全基于神经网络的完整两体势能表面的拟合
Accurate neural-network-based fitting of full-dimensional two-body potential energy surfaces
论文作者
论文摘要
我们描述了具有分子模拟的柔性单体的机器学习电位的发展。最近建议的一种排序不变的多项式神经网络（PIP-NN）方法用于代表二聚体能量的全维两体分量。为了确保渐近零交互极限，使用了整个不变多项式基集的量身定制子集，并修改了它们的变量以在远距离上更好地拟合正确的渐近行为。新技术用于为两体n $ _2- $ ar和n $ _2- $ ch $ _4 $交互作用来建立全维电位，这是通过安装在理论群体群体中计算出的Ab Itible Energies的数据库来建立的。然后，使用PIP-NN潜在表面在经典框架内计算出第二个具有分子柔韧性效应的病毒系数。为了展示PIP-NN方法的优势，我们将其精度和计算效率与基于内核和基于神经网络的几种基于乙醇的能量和力数据库进行了比较。对于大型训练组，PIP-NN模型在检查模型中达到了最佳准确性，并且计算时间与PIP回归模型的计算时间相当，并且比最快的替代方案快几个数量级。
We describe the development of machine-learned potentials of atmospheric gases with flexible monomers for molecular simulations. A recently suggested permutationally invariant polynomial neural network (PIP-NN) approach is utilized to represent the full-dimensional two-body component of the dimer energy. To ensure the asymptotic zero-interaction limit, a tailored subset of the full invariant polynomial basis set is utilized and their variables are modified to achieve a better fit of the correct asymptotic behavior at a long range. The new technique is used to build full-dimensional potentials for the two-body N$_2-$Ar and N$_2-$CH$_4$ interactions by fitting databases of ab initio energies calculated at the coupled-cluster level of theory. The second virial coefficients with full account of molecular flexibility effects are then calculated within the classical framework using the PIP-NN potential surfaces. To showcase the advantages of the PIP-NN method, we compare its accuracy and computational efficiency to several kernel-based and neural-network-based approaches using the MD17 database of energies and forces for ethanol. For large training set sizes, the PIP-NN models attain the best accuracy among examined models, and the computation time is shown to be comparable to that of the PIP regression model and several orders of magnitude faster than the quickest alternatives.
