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ABSTRACT:
SUBMITTER: Roncaglia C
PROVIDER: S-EPMC9875306 | biostudies-literature | 2023 Jan
REPOSITORIES: biostudies-literature
Roncaglia Cesare C Ferrando Riccardo R
Journal of chemical information and modeling 20230103 2
We propose a scheme for the automatic separation (i.e., clustering) of data sets composed of several nanoparticle (NP) structures by means of Machine Learning techniques. These data sets originate from atomistic simulations, such as global optimizations searches and molecular dynamics simulations, which can produce large outputs that are often difficult to inspect by hand. By combining a description of NPs based on their local atomic environment with unsupervised learning algorithms, such as K-M ...[more]