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Machine-Learned Fragment-Based Energies for Crystal Structure Prediction.

David McDonagh1, Chris-Kriton Skylaris1, Graeme M Day1

  • 1School of Chemistry , University of Southampton , Highfield, Southampton , SO17 1BJ , United Kingdom.

Journal of Chemical Theory and Computation
|March 1, 2019
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Summary

This study introduces a machine learning method to enhance crystal structure prediction accuracy by correcting force field energies. This approach improves the reliability of identifying stable crystal polymorphs with reduced computational cost.

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Area of Science:

  • Computational chemistry
  • Materials science
  • Crystallography

Background:

  • Crystal structure prediction is crucial for materials discovery but limited by force field accuracy.
  • Accurate assessment of thermodynamic stability is challenging for complex systems.
  • Quantum mechanical methods are accurate but computationally expensive.

Purpose of the Study:

  • To develop a computationally efficient method for improving crystal structure prediction.
  • To enhance the accuracy of force field-based lattice energy calculations.
  • To enable wider application of fragment-based methods in crystal structure prediction.

Main Methods:

  • Correcting two-body interactions in force fields using higher-level theory (density functionals, MP2).
  • Employing a fragment-based approach to predict energy corrections with machine learning.
  • Utilizing atom-centered symmetry functions and Gaussian processes for predicting two-body interactions.

Main Results:

  • Corrected lattice energies significantly improved the ranking of known polymorphs.
  • Fragment corrections systematically enhanced the relative lattice energies of polymorphs.
  • Machine learning prediction of two-body interactions achieved high accuracy with reduced training data (10-20%), cutting costs by an order of magnitude.

Conclusions:

  • The developed machine learning approach substantially improves the accuracy and efficiency of crystal structure prediction.
  • This method facilitates more reliable polymorph screening and computer-guided materials discovery.
  • The cost reduction enables broader adoption of advanced computational methods in materials science.