基于深度张量神经网络的密度泛函紧束缚精确多体排斥势
Accurate Many-Body Repulsive Potentials for Density-Functional Tight-Binding from Deep Tensor Neural Networks
摘要：
我们将密度泛函紧束缚（DFTB）与深度张量神经网络（DTNN）相结合，以最大限度地发挥这两种方法在预测结构，能量和振动分子性质方面的优势。DTNN是用来学习一个非线性模型的本地化的多体原子间的排斥能，到目前为止，已处理在一个原子成对的方式在DFTB。大大提高了标准的DFTB和DTNN，所得到的DFTB-NN $_{\sf{rep}}$模型产生准确的预测的原子化和异构化能量，平衡几何形状，振动频率和二面角的旋转配置文件的各种有机分子相比，混合DFT-PBE 0功能。我们的研究结果强调了将半经验电子结构方法与物理激励的机器学习方法相结合来预测局部多体相互作用的高潜力。最后，我们讨论了DFTB-NN $_{\sf{rep}}$方法的未来进展，该方法可以为具有数万个原子的系统提供化学精确的电子结构计算。
英文摘要：
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.
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