论文标题
深度张量神经网络的密度功能紧密结合的准确多体排斥潜力
Accurate Many-Body Repulsive Potentials for Density-Functional Tight-Binding from Deep Tensor Neural Networks
论文作者
论文摘要
我们将密度功能性紧密结合（DFTB）与深张量神经网络（DTNN）结合在一起，以最大程度地提高两种方法在预测结构，能量和振动分子特性方面的强度。 DTNN用于学习局部多体性拒绝能量的非线性模型，到目前为止，该模型已在DFTB中以Atomwise的方式进行处理。基本上改进了标准DFTB和DTNN，由此产生的DFTB-NN $ _ {\ SF {REP}} $模型可以准确预测雾化和异构化能量，平衡几何，振动频率，振动频率和二面旋转的有机分子型与Hybers相比。我们的结果突出了将半经验电子结构方法与物理动机的机器学习方法相结合的高潜力，以预测局部的多体相互作用。最后，我们讨论了DFTB-NN $ _ {\ sf {rep}} $方法的未来进步，该方法可以启用具有数以万计原子的系统的化学精确的电子结构计算。
We combine density-functional tight-binding (DFTB) with deep tensor neural networks (DTNN) to maximize the strengths of both approaches in predicting structural, energetic, and vibrational molecular properties. The DTNN is used to learn a non-linear model for the localized many-body interatomic repulsive energy, which so far has been treated in an atom-pairwise manner in DFTB. Substantially improving upon standard DFTB and DTNN, the resulting DFTB-NN$_{\sf{rep}}$ model yields accurate predictions of atomization and isomerization energies, equilibrium geometries, vibrational frequencies and dihedral rotation profiles for a large variety of organic molecules compared to the hybrid DFT-PBE0 functional. Our results highlight the high potential of combining semi-empirical electronic-structure methods with physically-motivated machine learning approaches for predicting localized many-body interactions. We conclude by discussing future advancements of the DFTB-NN$_{\sf{rep}}$ approach that could enable chemically accurate electronic-structure calculations for systems with tens of thousands of atoms.
