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Updated: Oct 29, 2025

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Compact atomic descriptors enable accurate predictions via linear models.

Claudio Zeni1, Kevin Rossi2, Aldo Glielmo1

  • 1Physics Area, International School for Advanced Studies, Trieste, Italy.

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Summary
This summary is machine-generated.

We found that simple linear ridge regression accurately predicts material properties, matching complex machine learning models. Descriptor compression significantly improves efficiency without losing accuracy, paving the way for optimized material-agnostic descriptors.

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Area of Science:

  • Computational materials science
  • Machine learning in chemistry
  • Condensed matter physics

Background:

  • Accurate prediction of material properties like formation energies and atomic forces is crucial for materials discovery.
  • Current machine learning methods often require complex descriptors and significant computational resources.

Purpose of the Study:

  • To evaluate the accuracy of linear ridge regression with a three-body local density representation for predicting material properties.
  • To explore methods for descriptor sparsification and computational efficiency improvement.
  • To assess the potential for developing compressed, material-agnostic descriptors.

Main Methods:

  • Utilized a three-body local density representation derived from the atomic cluster expansion.
  • Employed linear ridge regression for fitting formation energies and atomic forces.
  • Applied principal component analysis (PCA) and least absolute shrinkage operator (LASSO) regression for descriptor sparsification.

Main Results:

  • Linear ridge regression achieved accuracy comparable to state-of-the-art, more complex machine learning methods.
  • Descriptor sparsification using PCA and LASSO reduced descriptor size by four times with maintained or improved accuracy.
  • Reduced descriptors exhibited shared features across multiple datasets, indicating potential for generalizability.

Conclusions:

  • Simple linear ridge regression offers a computationally efficient and accurate alternative for materials property prediction.
  • Descriptor compression is a viable strategy to enhance computational efficiency without sacrificing predictive power.
  • The findings suggest the feasibility of creating universally applicable, compressed, and accurate material descriptors.