TeaNet: Universal neural network interatomic potential inspired by iterative electronic relaxations
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1-s2.0-S0927025622000799-main.pdf
Description
Published version
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3.91 MB
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Checksum (MD5)
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Author(s)
Takamoto, So
Izumi, Satoshi
Li, Ju
Date Issued
2022
Journal
Computational Materials Science
Publisher
Elsevier BV
Citation
Takamoto, So, Izumi, Satoshi and Li, Ju. 2022. "TeaNet: Universal neural network interatomic potential inspired by iterative electronic relaxations." Computational Materials Science, 207.
Version
Final published version
Abstract
A universal interatomic potential for an arbitrary set of chemical elements
is urgently needed in computational materials science. Graph convolution neural
network (GCN) has rich expressive power, but previously was mainly employed to
transport scalars and vectors, not rank $\ge 2$ tensors. As classic interatomic
potentials were inspired by tight-binding electronic relaxation framework, we
want to represent this iterative propagation of rank $\ge 2$ tensor information
by GCN. Here we propose an architecture called the tensor embedded atom network
(TeaNet) where angular interaction is translated into graph convolution through
the incorporation of Euclidean tensors, vectors and scalars. By applying the
residual network (ResNet) architecture and training with recurrent GCN weights
initialization, a much deeper (16 layers) GCN was constructed, whose flow is
similar to an iterative electronic relaxation. Our traning dataset is generated
by density functional theory calculation of mostly chemically and structurally
randomized configurations. We demonstrate that arbitrary structures and
reactions involving the first 18 elements on the periodic table (H to Ar) can
be realized satisfactorily by TeaNet, including C-H molecular structures,
metals, amorphous SiO${}_2$, and water, showing surprisingly good performance
(energy mean absolute error 19 meV/atom) and robustness for arbitrary
chemistries involving elements from H to Ar.
MIT Department
Massachusetts Institute of Technology. Department of Nuclear Science and Engineering
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1016/J.COMMATSCI.2022.111280
