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• W4313488596 abstract "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-Means and Gaussian mixture model, we are able to distinguish between different structural motifs (e.g., icosahedra, decahedra, polyicosahedra, fcc fragments, twins, and so on). We show that this method is able to improve over the results obtained previously thanks to the successful implementation of a more detailed description of NPs, especially for systems showing a large variety of structures, including disordered ones." @default.
• W4313488596 created "2023-01-06" @default.
• W4313488596 creator A5038554157 @default.
• W4313488596 creator A5048817193 @default.
• W4313488596 date "2023-01-04" @default.
• W4313488596 modified "2023-10-18" @default.
• W4313488596 title "Machine Learning Assisted Clustering of Nanoparticle Structures" @default.
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• W4313488596 doi "https://doi.org/10.1021/acs.jcim.2c01203" @default.
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