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Determination of stable structure of a cluster using convolutional neural network and particle swarm optimization

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Abstract

Minimization of the energy of a molecule is an important research problem in quantum chemistry. The use of appropriate global optimization algorithms for determining the most stable configuration is a matter of active interest, and various efforts have been made toward achieving the same. Instead of using single method-based techniques, a recent method has been developed for constructing new models where particle swarm optimization can be made use of. In the present study, we propose a convolutional neural network (CNN) model for learning and predicting the energy of a system by training geometries of cluster units containing both metal and non-metal atoms, viz. C5, N42−, N64−, Aun (n = 2 − 8) and AunAgm (2 ≤ n + m ≤ 8) clusters as prototype examples. Initially, several random clusters are generated within a given range in the three-dimensional space and their energies determined using the atom-centered density matrix propagation molecular dynamics simulation (ADMP). A CNN model is constructed from this initial set of clusters, which is later used for generating a huge number of systems required while searching for the stable structure, instead of using time-consuming quantum mechanical calculations in an iterative process. While several global optimization algorithms could have been used, we choose to employ particle swarm optimization (PSO) due to its ease of implementation and efficient rate of convergence.

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Acknowledgments

We are happy to dedicate this article to Professor Ramon Carbó-Dorca, a long-time friend of PKC, on his 80th birth anniversary. We would like to thank Professors Gernot Frenking, Miquel Solà and Tanmoy Chakraborty for kindly inviting us to contribute this article to the Special Issue of the Theoretical Chemistry Accounts. PKC thanks the DST, New Delhi for his J. C. Bose National Fellowship. SS thanks CSE for the computational facilities. AM thanks Department of Computer Science and Engineering, IIT Kharagpur for the computational facilities. GJ and RP thank IIT Kharagpur and CSIR, respectively, for their fellowships. PG thanks Department of Chemistry, IIT Kharagpur.

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  1. Department of Electronics and Electrical Communication Engineering, Indian Institute of Technology, Kharagpur, India

    Arka Mitra

  2. Department of Chemistry, Indian Institute of Technology, Kharagpur, 721302, India

    Gourhari Jana, Pratiksha Gaikwad & Pratim Kumar Chattaraj

  3. Advanced Technology Development Centre, Indian Institute of Technology, Kharagpur, 721302, India

    Ranita Pal

  4. Department of Computer Science and Engineering, Indian Institute of Technology, Kharagpur, 721302, India

    Shamik Sural

  5. Department of Chemistry, Indian Institute of Technology Bombay, Powai, Mumbai, 400076, India

    Pratim Kumar Chattaraj

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  1. Arka Mitra
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Mitra, A., Jana, G., Pal, R. et al. Determination of stable structure of a cluster using convolutional neural network and particle swarm optimization. Theor Chem Acc 140, 30 (2021). https://doi.org/10.1007/s00214-021-02726-z

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