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Guided Clustering for Selecting Representatives Samples in Chemical Databases

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Computational Science and Its Applications – ICCSA 2023 Workshops (ICCSA 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14111))

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Abstract

Machine Learning (ML) methods, from unsupervised to supervised algorithms, have been applied to solve several tasks in the Materials Science domain, such as property prediction, design of new chemical compounds, and surrogate models in molecular dynamics simulations. ML methods can also play a fundamental role in screening materials by reducing the number of compounds under scrutiny. This reduction assumes that compounds similarly represented by a given descriptor might have similar properties; thus, an unsupervised ML method, such as the K-Means algorithm, can cluster the data set and deliver a set of representative samples. However, this selection depends on the molecular representation that might not directly relate to the target property. Here, we propose a framework that lets the specialist select a set of representative samples in a guided fashion. In particular, a loop between a clustering algorithm (k-means) and an optimization method (Basin-Hopping) is implemented, which allows the system to learn feature weights to form more homogeneous clusters given the target property. The framework also offers other visual and textual functionalities to support the expert. We evaluate the proposed framework in two scenarios, and the results show that the guidance enhances clustering formations, both in coarse (few and big clusters) and fine (many small clusters) analyses.

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Acknowledgements

The authors gratefully acknowledge support from FAPESP (São Paulo Research Foundation) and Shell, projects No. \(2017/11631-2\), \(2018/21401-7\) and \(2022/09285-7\), and the strategic importance of the support given by ANP (Brazil’s National Oil, Natural Gas and Biofuels Agency) through the R &D levy regulation. The authors also thank for the infrastructure provided to our computer cluster by the Department of Information Technology - Campus São Carlos.

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Authors and Affiliations

  1. Institute of Science and Technology, Federal University of São Paulo, São José dos Campos, SP, Brazil

    Felipe V. Calderan & Marcos G. Quiles

  2. São Carlos Institute of Chemistry, University of São Paulo, PO Box 780, 13560-970, São Carlos, SP, Brazil

    João Paulo A. de Mendonça & Juarez L. F. Da Silva

Authors
  1. Felipe V. Calderan
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  2. João Paulo A. de Mendonça
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  3. Juarez L. F. Da Silva
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  4. Marcos G. Quiles
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Correspondence to Marcos G. Quiles .

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Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Italy

    Beniamino Murgante

  3. University of Minho, Braga, Portugal

    Ana Maria A. C. Rocha

  4. University of Cagliari, Cagliari, Italy

    Chiara Garau

  5. University of Basilicata, Potenza, Italy

    Francesco Scorza

  6. University of Massachusetts Medical School, Worcester, MA, USA

    Yeliz Karaca

  7. Polytechnic University of Bari, Bari, Italy

    Carmelo M. Torre

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Calderan, F.V., de Mendonça, J.P.A., Silva, J.L.F.D., Quiles, M.G. (2023). Guided Clustering for Selecting Representatives Samples in Chemical Databases. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2023 Workshops. ICCSA 2023. Lecture Notes in Computer Science, vol 14111. Springer, Cham. https://doi.org/10.1007/978-3-031-37126-4_10

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  • DOI: https://doi.org/10.1007/978-3-031-37126-4_10

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