Regression and clustering algorithms for AgCu nanoalloys: from mixing energy predictions to structure recognition

文献情報

出版日 2021-10-11

DOI 10.1039/D1CP02143E

インパクトファクター 3.676

著者

Cesare Roncaglia, Daniele Rapetti, Riccardo Ferrando

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要旨

The lowest-energy structures of AgCu nanoalloys are searched for by global optimization algorithms for sizes 100 and 200 atoms depending on composition. Even though the AgCu system is very weakly miscible in macroscopic samples, the mixing energy for these nanoalloys turns out to be clearly negative for both sizes, a result which is attributed to the stabilization of non-crystalline Cu@Ag core–shell structures at the nanoscale. The mixing energy is a quantity nowadays unknown in its functional form, so that its prediction may take advantage of machine learning techniques. A support vector regressor is then implemented to successfully predict the mixing energy of AgCu nanoalloys of both sizes. Moreover, with the help of unsupervised learning algorithms, it is shown that the automatic classification of such nanoalloys into different physically meaningful structural families is indeed possible. Finally, thanks to the harmonic superposition approximation, the temperature-dependent probabilities of such structural families are calculated.

掲載誌

Physical Chemistry Chemical Physics

CiteScore: 5.5

自己引用率: 10.3%

年間論文数: 3036

Physical Chemistry Chemical Physics (PCCP) is an international journal co-owned by 19 physical chemistry and physics societies from around the world. This journal publishes original, cutting-edge research in physical chemistry, chemical physics and biophysical chemistry. To be suitable for publication in PCCP, articles must include significant innovation and/or insight into physical chemistry; this is the most important criterion that reviewers and Editors will judge against when evaluating submissions. The journal has a broad scope and welcomes contributions spanning experiment, theory, computation and data science. Topical coverage includes spectroscopy, dynamics, kinetics, statistical mechanics, thermodynamics, electrochemistry, catalysis, surface science, quantum mechanics, quantum computing and machine learning. Interdisciplinary research areas such as polymers and soft matter, materials, nanoscience, energy, surfaces/interfaces, and biophysical chemistry are welcomed if they demonstrate significant innovation and/or insight into physical chemistry. Joined experimental/theoretical studies are particularly appreciated when complementary and based on up-to-date approaches.