TeaNet: universal neural network interatomic potential inspired by iterative electronic relaxations
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A universal interatomic potential applicable to arbitrary elements and structures is urgently needed in computational materials science. Graph convolution-based neural network is a promising approach by virtue of its ability to express complex relations. Thus far, it has been thought to represent a completely different approach from physics-based interatomic potentials. In this paper, we show that these two methods can be regarded as different representations of the same tight-binding electronic relaxation framework, where atom-based and overlap integral or "bond"-based Hamiltonian information are propagated in a directional fashion. Based on this unified view, we propose a new model, named the tensor embedded atom network (TeaNet), where the stacked network model is associated with the electronic total energy relaxation calculation. Furthermore, Tersoff-style angular interaction is translated into graph convolution architecture through the incorporation of Euclidean tensor values. Our model can represent and transfer spatial information. TeaNet shows great performance in both the robustness of interatomic potentials and the expressive power of neural networks. We demonstrate that arbitrary chemistry involving the first 18 elements on the periodic table (H to Ar) can be realized by our model, including C-H molecular structures, metals, amorphous SiO_2, and water.
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