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Andreas Møller Slavensky1, Mads-Peter V Christiansen1, Bjørk Hammer1

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Summary
This summary is machine-generated.

This study introduces complementary energy (CE) landscapes to improve atomistic structure optimization. By simplifying potential energy surfaces (PES), this method aids in discovering new global minimum energy structures more efficiently.

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Area of Science:

  • Computational materials science
  • Chemical physics
  • Machine learning in chemistry

Background:

  • Global optimization of atomistic structures is crucial for discovering new materials.
  • Exploring the potential energy surface (PES) to find the global minimum is computationally challenging.
  • Current methods require efficient candidate structure generation to navigate complex PES.

Purpose of the Study:

  • To introduce and evaluate a novel structure generation strategy using complementary energy (CE) landscapes.
  • To demonstrate how optimizing structures in CE landscapes can aid in global optimization.
  • To report a new global minimum energy structure for an olivine (Mg2SiO4)4 cluster.

Main Methods:

  • Formulating temporary complementary energy (CE) landscapes using machine-learned potentials (MLPs).
  • Constructing deliberately incomplete MLPs that are smoother than the true PES.
  • Locally optimizing candidate structures within these CE landscapes.
  • Testing the approach on a reduced rutile SnO2(110)-(4 × 1) surface and an olivine (Mg2SiO4)4 cluster.

Main Results:

  • CE landscapes, as simplified MLPs, possess fewer local minima, facilitating smoother exploration.
  • Local optimization in CE landscapes effectively guides the search towards new funnels in the true PES.
  • A new global minimum energy structure was identified for the (Mg2SiO4)4 cluster.

Conclusions:

  • Complementary energy landscapes offer a promising strategy for enhancing global optimization of atomistic structures.
  • This method can accelerate the discovery of stable material configurations.
  • The approach is validated by successful application to complex surface and cluster systems.