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Updated: Apr 3, 2026

Spatial Separation of Molecular Conformers and Clusters

Spatial Separation of Molecular Conformers and Clusters

Published on: January 9, 2014

Permutation-invariant distance between atomic configurations.

Grégoire Ferré¹, Jean-Bernard Maillet¹, Gabriel Stoltz²

¹CEA, DAM, DIF, F-91297 Arpajon, France.

The Journal of Chemical Physics

|September 17, 2015

Summary

This summary is machine-generated.

We developed a novel, permutation-invariant distance metric for atomic configurations. This method directly compares atomic environments without needing intermediate fingerprints, offering efficient structural analysis and descriptor evaluation.

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Area of Science:

Materials Science
Computational Chemistry
Physics

Background:

Comparing atomic configurations is crucial for understanding material properties.
Existing methods often rely on dimensionality reduction (fingerprints) or computationally expensive minimization steps.
A direct, invariant distance metric is needed for efficient analysis.

Purpose of the Study:

To introduce a novel permutation-invariant distance metric for atomic configurations.
To enable direct comparison of atomic environments with varying numbers of particles.
To provide a robust framework for evaluating structural information and similarity.

Main Methods:

Defining a functional representation of atomic positions.
Implementing permutation invariance directly into the distance calculation.
Utilizing Monte Carlo simulated annealing for global rotation invariance.
Establishing a formal metric space framework for the proposed distance.

Main Results:

The proposed distance metric directly compares atomic configurations without intermediate fingerprints.
It naturally handles permutation invariance, avoiding time-consuming minimization.
Global rotation invariance is achieved through simulated annealing.
The distance is formally shown to be a metric on the space of atomic configurations.

Conclusions:

The new distance metric offers a computationally efficient and robust way to compare atomic configurations.
It is effective for evaluating the faithfulness of atomic fingerprints (descriptors).
The metric excels in structural analysis, discriminating local structures and classifying similarities.