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

Maicon Pierre Lourenço1, Lizandra Barrios Herrera2, Jiří Hostaš2

  • 1Departamento de Química e Física - Centro de Ciências Exatas, Naturais e da Saúde - CCENS - Universidade Federal do Espírito Santo, 29500-000, Alegre, Espírito Santo, Brasil. maiconpl01@gmail.com.

Journal of Molecular Modeling
|June 2, 2022
PubMed
Summary

An artificial intelligence approach using active learning optimizes adsorbate-surface interactions for materials science. This method efficiently finds optimal adsorption structures, reducing computational costs and accelerating material discovery.

Keywords:
Active learningAdsorptionFunctional materialsMachine learningSCC-DFTB

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Area of Science:

  • Computational materials science
  • Surface science
  • Artificial intelligence in chemistry

Background:

  • Adsorbate-substrate interactions are crucial for functional materials, supramolecular chemistry, and solvent interactions.
  • In silico modeling of these systems is challenging due to the vast number of possible configurations.
  • Accurate prediction of adsorption geometry and energy is essential for technological applications.

Purpose of the Study:

  • To develop and implement an artificial intelligence (AI) based active learning (AL) method for optimizing adsorbate adsorption on material surfaces.
  • To enable accurate and automated structural elucidation of adsorbates on surfaces by minimizing total electronic energy.
  • To enhance the efficiency of computational material design and discovery.

Main Methods:

  • Developed an active learning (AL) framework utilizing machine learning (ML) regression algorithms and uncertainty quantification.
  • Implemented the AL method within the QMLMaterial software for automated structural elucidation.
  • Employed SCC-DFTB calculations for surface energy calculations and an artificial neural network (NN) with K-fold cross-validation and bootstrap resampling for uncertainty quantification.
  • Utilized expected improvement (EI) and lower confidence bound (LCB) as acquisition functions for decision-making.

Main Results:

  • The developed AL method successfully optimized adsorption structures for C60@TiO2 anatase (101).
  • The approach demonstrated increased probability of finding the global minimum with a reduced number of calculations.
  • The methodology provides accurate and automated structural elucidation of adsorbates on surfaces.
  • The QMLMaterial software now includes this advanced feature for material design.

Conclusions:

  • The AI-driven active learning approach offers an efficient and accurate method for modeling adsorbate-substrate interactions.
  • This technique significantly reduces the computational cost associated with exploring complex adsorption landscapes.
  • The developed tool accelerates the discovery and design of novel functional materials by automating structural elucidation.