Transferable prediction of formation energy across lattices of increasing size

12 July 2023, Version 2
This content is a preprint and has not undergone peer review at the time of posting.

Abstract

In this study, we show the transferability of graph convolutional neural network (GCNN) predictions of the formation energy of the nickel-platinum (NiPt) solid solution alloy across atomic structures of increasing sizes. The original dataset was generated with the Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) using the second nearest-neighbor modified embedded-atom method (2NN MEAM) empirical interatomic potential. Geometry optimization was performed on the initially randomly generated face centered cubic (FCC) crystal structures and the formation energy has been calculated at each step of the geometry optimization, with configurations spanning the whole compositional range. Using data from various steps of geometry optimization, we first trained the GCNN on a lattice of 256 atoms, which accounts well for the short-range interactions. Using this data, we predicted the formation energy for lattices of 864 atoms and 2,048 atoms, which resulted in lower-than-expected accuracy due to the long-range interactions present in these larger lattices. We accounted for the long-range interactions by including a small amount of training data representative for those two larger sizes, whereupon the predictions of the GCNN scaled linearly with the size of the lattice. Therefore, our strategy ensured scalability while reducing significantly the computational cost of training on larger lattice sizes.

Keywords

Graph Neural Networks
Deep Learning
Solid Solution Alloys

Supplementary materials

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Title
Supplementary Material: Transferable prediction of formation energy across lattices of increasing size
Description
This Supplementary Material document contains quantitative comparisons between different graph neural network (GNN) models trained on different datasets, as well as additional scatterplots that have not been included in the manuscript.
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