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Physics-Inspired Structural Representations for Molecules and Materials.

Felix Musil1,2, Andrea Grisafi1, Albert P Bartók3

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Machine learning in chemistry and materials science relies on effective atomic-scale representations. This review explores common structural and chemical descriptions, linking them to properties and computational efficiency for broader applications.

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Area of Science:

  • Computational chemistry and materials science
  • Machine learning applications in science

Background:

  • Predicting material properties requires transforming atomic coordinates into suitable representations.
  • Atomic-scale representations are crucial for the success of machine learning (ML) in chemistry and materials science.

Purpose of the Study:

  • To review common structural and chemical descriptions of atomistic structures.
  • To highlight connections between different representation frameworks.
  • To emphasize the link between physical chemistry, structure, properties, and their mathematical descriptions.

Main Methods:

  • Literature review of commonly used atomic-scale representations.
  • Analysis of connections between different representational frameworks.
  • Discussion of computational efficiency and applicability of models.

Main Results:

  • Identified commonalities and underlying principles across various atomic-scale representations.
  • Demonstrated the link between physical chemistry, structure, properties, and mathematical descriptions.
  • Provided examples of ML applications in diverse chemical and materials science problems.

Conclusions:

  • Computationally efficient and universally applicable models are key for advancing ML in chemistry and materials science.
  • Understanding the relationship between structure, properties, and their mathematical representation is vital.
  • Future research should focus on open questions and promising directions in atomic-scale representation development.