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Title: Machine-learning-accelerated multimodal characterization and multiobjective design optimization of natural porous materials

Abstract

Natural porous materials such as nanoporous clays are used as green and low-cost adsorbents and catalysts. The key factors determining their performance in these applications are the pore morphology and surface activity, which are typically represented by properties such as specific surface area, pore volume, micropore content and pH. The latter may be modified and tuned to specific applications through material processing and/or chemical treatment. Characterization of the material, raw or processed, is typically performed experimentally, which can become costly especially in the context of tuning of the properties towards specific application requirements and needing numerous experiments. In this work, we present an application of tree-based machine learning methods trained on experimental datasets to accelerate the characterization of natural porous materials. The resulting models allow reliable prediction of the outcomes of experimental characterization of processed materials (R2 from 0.78 to 0.99) as well as identification of key factors contributing to those properties through feature importance analysis. Furthermore, the high throughput of the models enables exploration of processing parameter–property correlations and multiobjective optimization of prototype materials towards specific applications. We have applied these methodologies to pinpoint and rationalize optimal processing conditions for clays exploitable in acid catalysis. One of such identifiedmore » materials was synthesized and tested revealing appreciable acid character improvement with respect to the pristine material. Specifically, it achieved 79% removal of chlorophyll-a in acid catalyzed degradation.« less

Authors:
ORCiD logo [1]; ORCiD logo [2];  [2]; ORCiD logo [3]
  1. IMDEA Materials Inst., Getafe (Spain); Universidad Carlos III de Madrid (Spain). Dept. of Materials Science and Engineering and Chemical Engineering; Tolsa Group, Getafe (Spain)
  2. Tolsa Group, Getafe (Spain)
  3. IMDEA Materials Inst., Getafe (Spain)
Publication Date:
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES). Chemical Sciences, Geosciences & Biosciences Division
OSTI Identifier:
1816672
Grant/Contract Number:  
AC02-05CH11231
Resource Type:
Accepted Manuscript
Journal Name:
Chemical Science
Additional Journal Information:
Journal Volume: 12; Journal Issue: 27; Journal ID: ISSN 2041-6520
Publisher:
Royal Society of Chemistry
Country of Publication:
United States
Language:
English
Subject:
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY; 97 MATHEMATICS AND COMPUTING

Citation Formats

Lo Dico, Giulia, Nuñez, Álvaro Peña, Carcelén, Verónica, and Haranczyk, Maciej. Machine-learning-accelerated multimodal characterization and multiobjective design optimization of natural porous materials. United States: N. p., 2021. Web. doi:10.1039/d1sc00816a.
Lo Dico, Giulia, Nuñez, Álvaro Peña, Carcelén, Verónica, & Haranczyk, Maciej. Machine-learning-accelerated multimodal characterization and multiobjective design optimization of natural porous materials. United States. https://doi.org/10.1039/d1sc00816a
Lo Dico, Giulia, Nuñez, Álvaro Peña, Carcelén, Verónica, and Haranczyk, Maciej. Wed . "Machine-learning-accelerated multimodal characterization and multiobjective design optimization of natural porous materials". United States. https://doi.org/10.1039/d1sc00816a. https://www.osti.gov/servlets/purl/1816672.
@article{osti_1816672,
title = {Machine-learning-accelerated multimodal characterization and multiobjective design optimization of natural porous materials},
author = {Lo Dico, Giulia and Nuñez, Álvaro Peña and Carcelén, Verónica and Haranczyk, Maciej},
abstractNote = {Natural porous materials such as nanoporous clays are used as green and low-cost adsorbents and catalysts. The key factors determining their performance in these applications are the pore morphology and surface activity, which are typically represented by properties such as specific surface area, pore volume, micropore content and pH. The latter may be modified and tuned to specific applications through material processing and/or chemical treatment. Characterization of the material, raw or processed, is typically performed experimentally, which can become costly especially in the context of tuning of the properties towards specific application requirements and needing numerous experiments. In this work, we present an application of tree-based machine learning methods trained on experimental datasets to accelerate the characterization of natural porous materials. The resulting models allow reliable prediction of the outcomes of experimental characterization of processed materials (R2 from 0.78 to 0.99) as well as identification of key factors contributing to those properties through feature importance analysis. Furthermore, the high throughput of the models enables exploration of processing parameter–property correlations and multiobjective optimization of prototype materials towards specific applications. We have applied these methodologies to pinpoint and rationalize optimal processing conditions for clays exploitable in acid catalysis. One of such identified materials was synthesized and tested revealing appreciable acid character improvement with respect to the pristine material. Specifically, it achieved 79% removal of chlorophyll-a in acid catalyzed degradation.},
doi = {10.1039/d1sc00816a},
journal = {Chemical Science},
number = 27,
volume = 12,
place = {United States},
year = {Wed Jun 02 00:00:00 EDT 2021},
month = {Wed Jun 02 00:00:00 EDT 2021}
}

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