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We present the details of the numerical realization of the recently advanced algorithm developed to identify the fragmentation in heavy ion reactions. This new algorithm is based on the Simulated Annealing method and is dubbed as Simulated…

Nuclear Theory · Physics 2009-10-31 Rajeev K. Puri , Joerg Aichelin

A study of multifragmentation of gold nuclei is reported at incident energies of 400, 600 and 1000 MeV/nucleon using microscopic theory. The present calculations are done within the framework of quantum molecular dynamics (QMD) model. The…

Nuclear Theory · Physics 2010-12-17 Yogesh K. Vermani , Rajeev K. Puri

The characteristics of fragment emission in peripheral $^{197}$Au+$^{197}$Au collisions 35 MeV/A are studied using the two clusterization approaches within framework of \emph{quantum molecular dynamics} model. Our model calculations using…

Nuclear Theory · Physics 2011-12-02 Yogesh K. Vermani , Rajeev K. Puri

We study the formation of fragments by extending the minimum spanning tree method (MST) for clusterization. In this extension, each fragment is subjected to a binding-energy check calculated using the modified Bethe-Weizsacker formula.…

Nuclear Theory · Physics 2011-06-24 Supriya Goyal , Rajeev K. Puri

Clusterization in phase space has been analyzed for peripheral Au+Au reactions at 1000 AMeV using simulated annealing clusterization algorithm (SACA). We investigate how these fragments are correlated in phase space and compare our model…

Nuclear Theory · Physics 2011-08-09 Yogesh K. Vermani

We present an algorithm for quantum-assisted cluster analysis (QACA) that makes use of the topological properties of a D-Wave 2000Q quantum processing unit (QPU). Clustering is a form of unsupervised machine learning, where instances are…

Quantum Physics · Physics 2018-03-09 Florian Neukart , David Von Dollen , Christian Seidel

The objective of this research is to explore the formation/binding energetics and length scales associated with the interaction between He$_n$V clusters and grain boundaries in bcc $\alpha$-Fe. In this work, we calculated formation/binding…

Materials Science · Physics 2014-06-18 M. A. Tschopp , F. Gao , K. N. Solanki

Clustering algorithms are fundamental tools across many fields, with density-based methods offering particular advantages in identifying arbitrarily shaped clusters and handling noise. However, their effectiveness is often limited by the…

Machine Learning · Computer Science 2025-12-01 Meysam Shirdel Bilehsavar , Razieh Ghaedi , Samira Seyed Taheri , Xinqi Fan , Christian O'Reilly

In a recent paper, J. Chem. Phys. 162, 214101 (2025), a novel approach for the rigidification of a molecular cluster was proposed, in which starting with an all-atom (AA) potential, a coarse-grained (CG) potential for the associated cluster…

Chemical Physics · Physics 2025-09-08 João V. M. Pimentel , Vladimir A. Mandelshtam

We apply the conformational space annealing (CSA) method to the Lennard-Jones clusters and find all known lowest energy configurations up to 201 atoms, without using extra information of the problem such as the structures of the known…

Statistical Mechanics · Physics 2009-11-10 Julian Lee , In-Ho Lee , Jooyoung Lee

Dynamics of spectator matter break-up in non-central collisions of $^{197}$Au+ $^{197}$Au at 1000 AMeV are explored within framework of quantum molecular dynamics (QMD) model. The phase space of nucleons bound in intermediate mass fragments…

Nuclear Theory · Physics 2015-05-28 Yogesh K. Vermani , Ashok Jangid

Clustering is a fundamental problem in many scientific applications. Standard methods such as $k$-means, Gaussian mixture models, and hierarchical clustering, however, are beset by local minima, which are sometimes drastically suboptimal.…

Machine Learning · Statistics 2015-12-14 Eric C. Chi , Kenneth Lange

This paper studies quantum annealing (QA) for clustering, which can be seen as an extension of simulated annealing (SA). We derive a QA algorithm for clustering and propose an annealing schedule, which is crucial in practice. Experiments…

Artificial Intelligence · Computer Science 2014-08-12 Kenichi Kurihara , Shu Tanaka , Seiji Miyashita

This paper studies quantum annealing (QA) for clustering, which can be seen as an extension of simulated annealing (SA). We derive a QA algorithm for clustering and propose an annealing schedule, which is crucial in practice. Experiments…

Disordered Systems and Neural Networks · Physics 2009-05-28 Kenichi Kurihara , Shu Tanaka , Seiji Miyashita

We study the cooperative rupture of multiple adhesion bonds under shared linear loading. Simulations of the appropriate Master equation are compared with numerical integration of a rate equation for the mean number of bonds and its scaling…

Soft Condensed Matter · Physics 2007-05-23 T. Erdmann , U. S. Schwarz

A new open-source cluster finding library ''Common Clusterization Library'' (CCL) is proposed to describe the clusters production when applied to the transport codes. The new library was applied to the Parton-Hadron-Quantum-Molecular…

Nuclear Theory · Physics 2026-01-13 Viktar Kireyeu

We present a novel framework exploiting the cascade of phase transitions occurring during a simulated annealing of the Expectation-Maximisation algorithm to cluster datasets with multi-scale structures. Using the weighted local covariance,…

Machine Learning · Statistics 2021-01-13 T. Bonnaire , A. Decelle , N. Aghanim

Symplectic symmetry approach to clustering (SSAC) in atomic nuclei, recently proposed, is modified and further developed in more detail. It is firstly applied to the light two-cluster $^{20}$Ne + $\alpha$ system of $^{24}$Mg, the latter…

Nuclear Theory · Physics 2025-02-18 H. G. Ganev

What is the origin of nuclear clustering and how does it emerge from the nuclear interaction? While there is ample experimental evidence for this phenomenon, its theoretical characterization directly from nucleons as degrees of freedom…

Nuclear Theory · Physics 2020-10-20 K. Fossez

Pairwise clustering, in general, partitions a set of items via a known similarity function. In our treatment, clustering is modeled as a transductive prediction problem. Thus rather than beginning with a known similarity function, the…

Machine Learning · Computer Science 2017-06-21 Stephen Pasteris , Fabio Vitale , Claudio Gentile , Mark Herbster
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