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Related Experiment Video

Updated: Dec 4, 2025

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

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Published on: April 8, 2020

Sensitivity and dimensionality of atomic environment representations used for machine learning interatomic

Berk Onat¹, Christoph Ortner², James R Kermode¹

¹Warwick Centre for Predictive Modelling, School of Engineering, University of Warwick, Coventry CV4 7AL, United Kingdom.

The Journal of Chemical Physics

|October 22, 2020

Summary

This summary is machine-generated.

Machine learning for materials requires accurate atomic environment representations. This study classifies representations, finding they are sensitive to perturbations but can be compressed, improving model accuracy.

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Area of Science:

Computational materials science
Machine learning for chemistry
Data representation

Background:

Accurate representation of atomic environments is crucial for machine learning models in materials science and chemistry.
Various methods exist, including atom-centered symmetry functions and Chebyshev Polynomial Symmetry Functions (CHSF).

Purpose of the Study:

To systematically classify atomic environment representations for machine learning.
To investigate their sensitivity to perturbations and effective dimensionality.
To evaluate their performance on material datasets.

Main Methods:

Classification of atomic environment representations.
Analysis of sensitivity to tangential perturbations.
Dimensionality reduction and subset selection.
Regression modeling on material datasets.

Main Results:

No investigated atomic environment representations were found to be linearly stable under tangential perturbations.
Chebyshev Polynomial Symmetry Functions (CHSF) showed instabilities, addressable by redefining the representation.
Most representations can be compressed without precision loss.
Optimizing subsets of representations enhances regression model accuracy.

Conclusions:

Atomic environment representations require careful consideration of stability and dimensionality for reliable machine learning applications.
Compression and feature selection are effective strategies for improving model performance and efficiency.