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An atom comprises protons and neutrons, which are contained inside the dense, central core called the nucleus, with electrons present around the nucleus. Taking into account the wave–particle duality of electrons and the uncertainty in position around the nucleus, quantum mechanics provides a more accurate model for the atomic structure. It describes atomic orbitals as the regions around the nucleus where electrons of discrete energy exist, characterized by four quantum...
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Exploring the phase change and structure of carbon using a deep learning interatomic potential.

Kai Chen1, Riyi Yang1, Zhefeng Wang1

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A new machine learning potential for carbon enables efficient, accurate simulations of large-scale systems. This breakthrough facilitates studying carbon phase transitions and discovering new carbon structures under extreme conditions.

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Area of Science:

  • Materials Science
  • Computational Chemistry
  • Condensed Matter Physics

Background:

  • Accurately simulating phase transitions in large-scale systems is crucial for understanding materials behavior.
  • Traditional methods like *ab initio* molecular dynamics (AIMD) are accurate but computationally expensive.
  • Empirical potentials offer speed but lack the necessary precision for phase transition studies.

Purpose of the Study:

  • To develop a computational method that combines efficiency and precision for studying phase transitions in large-scale carbon systems.
  • To investigate the formation mechanisms of amorphous and polycrystalline diamond from C60 and graphene precursors.
  • To discover novel carbon structures using advanced computational tools.

Main Methods:

  • Development of a machine learning potential (MLP) for carbon using deep neural networks.
  • Simulations of large-scale systems under high-pressure, high-temperature (HPHT) conditions.
  • Utilizing structure search software (AIRSS) for generating initial structures, followed by optimization with the MLP.

Main Results:

  • The developed MLP demonstrates strong scalability and enables efficient study of carbon phase transitions.
  • Successfully elucidated formation mechanisms of amorphous and polycrystalline diamond.
  • Identified new carbon structural clusters, with MLP predictions aligning with Gaussian approximation potential (GAP) but with superior computational efficiency.

Conclusions:

  • The novel MLP offers a powerful and efficient tool for carbon material research, particularly for phase transition studies.
  • Significant progress in understanding carbon material behavior under extreme conditions.
  • Opens new avenues for exploring carbon allotropes and their properties with enhanced computational efficacy.