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A new active learning approach for adsorbate–substrate structural elucidation in silico

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Abstract

Adsorbate interactions with substrates (e.g. surfaces and nanoparticles) are fundamental for several technologies, such as functional materials, supramolecular chemistry, and solvent interactions. However, modeling these kinds of systems in silico, such as finding the optimum adsorption geometry and energy, is challenging, due to the huge number of possibilities of assembling the adsorbate on the surface. In the current work, we have developed an artificial intelligence (AI) approach based on an active learning (AL) method for adsorption optimization on the surface of materials. AL uses machine learning (ML) regression algorithms and their uncertainties to make a decision (based on a policy) for the next unexplored structures to be computed, increasing, though, the probability of finding the global minimum with a small number of calculations. The methodology allows an accurate and automated structural elucidation of the adsorbate on the surface, based on the minimization of the total electronic energy. The new AL method for adsorption optimization was developed and implemented in the quantum machine learning software/agent for material design and discovery (QMLMaterial) program and was applied for C60@TiO2 anatase (101). It marks another software extension with a new feature in addition to the automatic structural elucidation of defects in materials and of nanoparticles as well. SCC-DFTB calculations were used to build the complex search surfaces with a reasonably low computational cost. An artificial neural network (NN) was employed in the AL framework evaluated together with two uncertainty quantification methods: K-fold cross-validation and non-parametric bootstrap (BS) resampling. Also, two different acquisition functions for decision-making were used: expected improvement (EI) and the lower confidence bound (LCB).

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Data availability

The datasets generated and analysed during the current study are available from the corresponding author on reasonable request.

Code availability

The QMLMaterial software is available from the corresponding author on reasonable request.

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Acknowledgements

We acknowledge the Computational resources provided by the Centro Nacional de Processamento de Alto Desempenho em São Paulo (CENAPAD-SP). We are grateful to Compute Canada/WestGrid for computational resources. Work supported by the National Research Council of Canada, Artificial Intelligence for Design program and by the Natural Sciences and Engineering Research Council of Canada, Discovery Grant (RGPIN-2019-03976).

Funding

This work was supported by the Brazilian agencies: Fundação de Amparo à Pesquisa do Espírito Santo (FAPES) – project CNPq/FAPES PPP 22/2018, Conselho Nacional para o Desenvolvimento Científico e Tecnológico (CNPq), and Coordenação de Aperfeiçoamento de Pessoal de Ensino Superior (CAPES). AMK received funding from CONACYT through the project A1-S-11929.

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Authors and Affiliations

  1. Departamento de Química e Física – Centro de Ciências Exatas, Naturais e da Saúde – CCENS – Universidade Federal do Espírito Santo, 29500-000, Alegre, Espírito Santo, Brasil

    Maicon Pierre Lourenço

  2. Department of Chemistry, Department of Physics and Astronomy, Quantum Alberta, CMS Centre for Molecular Simulation, IQST Institute for Quantum Science and Technology, University of Calgary, 2500 University Drive NW, Calgary, AB, T2N 1N4, Canada

    Lizandra Barrios Herrera, Jiří Hostaš & Dennis R. Salahub

  3. Departamento de Química, CINVESTAV, Av. Instituto Politécnico Nacional 2508, AP 14-740, México City, D.F., 07000, México

    Patrizia Calaminici & Andreas M. Köster

  4. Digital Technologies Research Centre, National Research Council of Canada, 1200 Montréal Road, Ottawa, ON, K1A 0R6, Canada

    Alain Tchagang

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  1. Maicon Pierre Lourenço
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  2. Lizandra Barrios Herrera
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Correspondence to Maicon Pierre Lourenço.

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Lourenço, M.P., Herrera, L.B., Hostaš, J. et al. A new active learning approach for adsorbate–substrate structural elucidation in silico. J Mol Model 28, 178 (2022). https://doi.org/10.1007/s00894-022-05173-0

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