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Found 10 papers, 3 papers with code
Accelerating Material Property Prediction using Generically Complete Isometry Invariants
Material or crystal property prediction using machine learning has grown popular in recent years as it provides a computationally efficient replacement to classical simulation methods.
Recognizing Rigid Patterns of Unlabeled Point Clouds by Complete and Continuous Isometry Invariants with no False Negatives and no False Positives
Rigid structures such as cars or any other solid objects are often represented by finite clouds of unlabeled points.
Compact Graph Representation of crystal structures using Point-wise Distance Distributions
Use of graphs to represent crystal structures has become popular in recent years as they provide a natural translation from atoms and bonds to nodes and edges.
Entropic trust region for densest crystallographic symmetry group packings
Molecular crystal structure prediction (CSP) seeks the most stable periodic structure given the chemical composition of a molecule and pressure-temperature conditions.
Fast predictions of lattice energies by continuous isometry invariants of crystal structures
Crystal Structure Prediction (CSP) aims to discover solid crystalline materials by optimizing periodic arrangements of atoms, ions or molecules.
The asymptotic behaviour and a near linear time algorithm for isometry invariants of periodic sets
The fundamental model of a periodic structure is a periodic point set up to rigid motion or isometry.
Materials Science 51-08 (Primary) 51K05 14L24, 74E15 (Secondary) G.0; J.2; I.3.5
Resolution-independent meshes of super pixels
Such a mesh of polygons can be rendered at any higher resolution with all edges kept straight.
Book embeddings of Reeb graphs
Let the nodes of $Reeb(f, X)$ be all homologically critical points where any homology of the corresponding component of the level set $f^{-1}(t)$ changes.
A fast and robust algorithm to count topologically persistent holes in noisy clouds
The holes in a given cloud are quantified by the topological persistence of their boundary contours when the cloud is analyzed at all possible scales.
Approximating persistent homology for a cloud of $n$ points in a subquadratic time
The Vietoris-Rips filtration for an $n$-point metric space is a sequence of large simplicial complexes adding a topological structure to the otherwise disconnected space.
