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An adaptive design approach for defects distribution modeling in materials from first-principle calculations

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Abstract

Designing and understanding the mechanism of non-stoichiometric materials with enhanced properties is challenging, both experimentally and even computationally, due to the large number of chemical spaces and their distributions through the material. In the current work, it is proposed a Machine Learning approach coupled with the Efficient Global Optimization (EGO) method—an Adaptive Design (AD)—to model local defects in materials from first-principle calculations. Our method takes into account the smallest sample set as possible, envisioning the material defect structure relationship with target properties for new insights. As an example, the AD framework allows us to study the stability and the structure of the modified goethite (Fe0.875Al0.125OOH) by considering a proper defect distribution, from first-principle calculations. The chemical space search for the modified goethite was evaluated by starting from different sizes and configurations of the samples as well as different surrogate models (ANN and Gaussian Process; GP), acquisition functions, and descriptors. Our results show that the same local solution of several defect arrangements in Fe0.875Al0.125OOH is found regardless of the initial sample and regression model. This indicates the efficiency of our search method. We also discuss the role of the descriptors in the accelerated global search for defects in material modeling. We conclude that the AD method applied in material defects is a successful approach in automating the search within huge chemical spaces from first-principle calculations by considering small samples. This method can be applied to mechanistic elucidation of non-stoichiometric materials, solid solutions, alloys, and Schottky and Frenkel defects, essential for material design and discovery.

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Acknowledgements

The support of 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) are gratefully acknowledged. Also, we acknowledge the Computational resources provided by the Centro Nacional de Processamento de Alto Desempenho em São Paulo (CENAPAD-SP) and by the Bremen Center for Computational Material Science (BCCMS). Financial support from the DFG grant Nr. DE1158/8-1, the Research Training Group grant DFG-RTG2247, and the Max-Planck-Institute for Structure and Dynamics of Matter (MPSD) are gratefully acknowledged by the author M. C. da Silva.

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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, Alegre, Espírito Santo, 29500-000, Brazil

    Maicon Pierre Lourenço & Alexandre dos Santos Anastácio

  2. Instituto de Física, Universidade Federal de Goiás, Av. Esperança, s/n - Campus Samambaia, Goiânia, GO, 74690-900, Brazil

    Andreia L. Rosa

  3. Bremen Center for Computational Materials Science, University of Bremen, P.O. Box 330440, 28334, Bremen, Germany

    Andreia L. Rosa, Thomas Frauenheim & Maurício Chagas da Silva

  4. Computational Science Research Center, Computational Science and Applied Research Institute Shenzhen, No.10 East Xibeiwang Road, 100193, Beijing, China

    Thomas Frauenheim

  5. Max Planck Institute for the Structure and Dynamics of Matter, Luruper Chaussee 149, Geb. 99 (CFEL), 22761, Hamburg, Germany

    Maurício Chagas da Silva

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  1. Maicon Pierre Lourenço
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  2. Alexandre dos Santos Anastácio
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  3. Andreia L. Rosa
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Correspondence to Maicon Pierre Lourenço.

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This paper belongs to Topical Collection XX - Brazilian Symposium of Theoretical Chemistry (SBQT2019)

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ESM 1

In the Support Information (SI), the following can be found: (1) Density of States of the pure and modified goethite; (2) a brief discussion about statistical regression as used in the current Adaptive Design approach; (3) the Expected Improvement (EI) plot (in 3D) as a function of (μ − fmax) and σ(x); and (4) additional results of the stability and structure of the modified goethite. (PDF 1.26 MB)

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Lourenço, M.P., dos Santos Anastácio, A., Rosa, A.L. et al. An adaptive design approach for defects distribution modeling in materials from first-principle calculations. J Mol Model 26, 187 (2020). https://doi.org/10.1007/s00894-020-04438-w

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