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Recognition of the three-dimensional structure of small metal nanoparticles by a supervised artificial neural network

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Abstract

Catalytic characteristics of metal nanoparticles heavily depend on their global shapes and sizes as well as on the structure and environment of catalytic sites. On the computational chemistry side, calculations of thermodynamic and kinetic data involve a high calculation cost which can be significantly lowered by the use of a trained machine learning model. This paper outlines a preliminary approach that aims at classifying the shape of the metal core of nanoparticles. Four different supervised artificial neural networks were trained, tested and submitted to a challenging dataset. They are based on two different structural descriptors, Coulomb matrices and radial distribution functions (RDFs). Each model is trained with hundreds of 3D models of nanoparticles that belong to eleven structural classes. The best model classifies a NP according to its discretized RDF profile and its first derivative. 100% accuracy is reached on the test stage, and up to 70% accuracy is obtained on the challenging dataset. It is mainly made of compounds that have global shapes significantly different from the training set. But some nonobvious structural patterns make then related to the eleven classes learned by the ANNs. Such strategy could easily be adapted to the recognition of NPs based on experimental neutron or X-ray diffraction data.

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Acknowledgements

We acknowledge the CALcul en MIdi-Pyrénées HPC (CALMIP-Olympe, grant P0611) for generous allocations of computer time. Université Paul Sabatier-Toulouse, INSAT and CNRS are also thanked for financial support. FJ and RP also thank Béatrice Laurent-Bonneau and Olivier Roustant for helpful and stimulating discussions. This article is dedicated to Fernand Spiegelmann on the occasion of his retirement. Short private message from RP: “Fernand, je ne te remercierai jamais assez de m’avoir donné la passion de ce métier, d’avoir su aiguiser ma curiosité scientifique, d’avoir contribué à ma formation large en chimie physique et théorique, et de m’avoir appris que développer à bon escient ses propres outils est une démarche féconde”.

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  1. LPCNO, UPS, CNRS (UMR 5215), Institut National des Sciences Appliquées (INSA), Université de Toulouse, 135 avenue de Rangueil, F-31077, Toulouse, France

    Timothée Fages, Franck Jolibois & Romuald Poteau

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  1. Timothée Fages
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  2. Franck Jolibois
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  3. Romuald Poteau
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Correspondence to Romuald Poteau.

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Fages, T., Jolibois, F. & Poteau, R. Recognition of the three-dimensional structure of small metal nanoparticles by a supervised artificial neural network . Theor Chem Acc 140, 98 (2021). https://doi.org/10.1007/s00214-021-02795-0

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