Abstract
The most fundamental model of a molecule is a cloud of unordered atoms, even without chemical bonds that can depend on thresholds for distances and angles. The strongest equivalence between clouds of atoms is rigid motion, which is a composition of translations and rotations. The existing datasets of experimental and simulated molecules require a continuous quantification of similarity in terms of a distance metric. While clouds of \( m \) ordered points were continuously classified by Lagrange’s quadratic forms (distance matrices or Gram matrices), their extensions to m unordered points are impractical due to the exponential number of \( m! \) permutations. We propose new metrics that are continuous in general position and are computable in a polynomial time in the number \( m \) of unordered points in any Euclidean space of a fixed dimension \( n \).
