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Predicting Lattice Vibrational Frequencies Using Deep Graph Neural Networks.

Nghia Nguyen1, Steph-Yves V Louis1, Lai Wei1

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|August 8, 2022
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Summary

Predicting crystal vibrational frequencies is crucial for materials science. A new deep graph neural network model efficiently estimates these frequencies from crystal structures, overcoming computational limitations of traditional methods.

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Area of Science:

  • Materials Science
  • Computational Physics
  • Machine Learning

Background:

  • Lattice vibrational frequencies significantly influence key material properties like thermal/electrical conductivity and superconductivity.
  • Traditional density functional theory (DFT) methods for calculating these frequencies are computationally intensive, limiting their use in large-scale materials screening.
  • Developing faster, accurate methods for predicting vibrational frequencies is essential for accelerating materials discovery.

Purpose of the Study:

  • To introduce a novel deep graph neural network (GNN) algorithm for predicting crystal vibrational frequencies.
  • To address the challenge of variable-dimension vibrational frequency spectra using a zero-padding scheme.
  • To evaluate the performance and transferability of the GNN model on diverse material datasets.

Main Methods:

  • Development of a deep graph neural network (GNN) algorithm tailored for crystal structure analysis.
  • Implementation of a zero-padding scheme to handle variable-dimension vibrational frequency spectra.
  • Benchmarking the GNN model on two large datasets (15,000 mixed-structure and 35,552 rhombohedra samples) and evaluating transferability on 239 cubic structures.

Main Results:

  • The GNN model achieved aggregated R-squared scores of 0.554 and 0.724 on the mixed-structure and rhombohedra datasets, respectively.
  • For individual cubic target structures, over 40% of predictions exceeded an R-squared score of 0.8, with a maximum of 0.98 using the mixed-sample trained model.
  • The average mean absolute error was 43.69 THz, indicating limitations in transferability across different structure types.

Conclusions:

  • Deep graph neural networks demonstrate significant potential for accurately predicting lattice vibration frequencies.
  • The proposed GNN algorithm offers a computationally efficient alternative to DFT for materials screening when sufficient training data is available.
  • Further research is needed to improve the model's transferability across diverse crystal structures.