Cargando…
A machine learning-based nano-photocatalyst module for accelerating the design of Bi(2)WO(6)/MIL-53(Al) nanocomposites with enhanced photocatalytic activity
It is a great challenge to acquire novel Bi(2)WO(6)/MIL-53(Al) (BWO/MIL) nanocomposites with excellent catalytic activity by the trial-and-error method in the vast untapped synthetic space. The degradation rate of Rhodamine B dye (DR(RhB)) can be used as the main parameter to evaluate the catalytic...
Autores principales: | , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
RSC
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10408574/ https://www.ncbi.nlm.nih.gov/pubmed/37560433 http://dx.doi.org/10.1039/d3na00122a |
_version_ | 1785086191512584192 |
---|---|
author | Zhai, Xiuyun Chen, Mingtong |
author_facet | Zhai, Xiuyun Chen, Mingtong |
author_sort | Zhai, Xiuyun |
collection | PubMed |
description | It is a great challenge to acquire novel Bi(2)WO(6)/MIL-53(Al) (BWO/MIL) nanocomposites with excellent catalytic activity by the trial-and-error method in the vast untapped synthetic space. The degradation rate of Rhodamine B dye (DR(RhB)) can be used as the main parameter to evaluate the catalytic activity of BWO/MIL nanocomposites. In this work, a machine learning-based nano-photocatalyst module was developed to speed up the design of BWO/MIL with desirable performance. Firstly, the DR(RhB) dataset was constructed, and four key features related to the synthetic conditions of BWO/MIL were filtered by the forward feature selection method based on support vector regression (SVR). Secondly, the SVR model with radical basis function for predicting the DR(RhB) of BWO/MIL was established with the key features and optimal hyperparameters. The correlation coefficients (R) between predicted and experimental DR(RhB) were 0.823 and 0.884 for leave-one-out cross-validation (LOOCV) and the external test, respectively. Thirdly, potential BWO/MIL nanocomposites with higher DR(RhB) were discovered by inverse projection, the prediction model, and virtual screening from the synthesis space. Meanwhile, an online web service (http://1.14.49.110/online_predict/BWO2) was built to share the model for predicting the DR(RhB) of BWO/MIL. Moreover, sensitivity analysis was brought into boosting the model's explainability and illustrating how the DR(RhB) of BWO/MIL changes over the four key features, respectively. The method mentioned here can provide valuable clues to develop new nanocomposites with the desired properties and accelerate the design of nano-photocatalysts. |
format | Online Article Text |
id | pubmed-10408574 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | RSC |
record_format | MEDLINE/PubMed |
spelling | pubmed-104085742023-08-09 A machine learning-based nano-photocatalyst module for accelerating the design of Bi(2)WO(6)/MIL-53(Al) nanocomposites with enhanced photocatalytic activity Zhai, Xiuyun Chen, Mingtong Nanoscale Adv Chemistry It is a great challenge to acquire novel Bi(2)WO(6)/MIL-53(Al) (BWO/MIL) nanocomposites with excellent catalytic activity by the trial-and-error method in the vast untapped synthetic space. The degradation rate of Rhodamine B dye (DR(RhB)) can be used as the main parameter to evaluate the catalytic activity of BWO/MIL nanocomposites. In this work, a machine learning-based nano-photocatalyst module was developed to speed up the design of BWO/MIL with desirable performance. Firstly, the DR(RhB) dataset was constructed, and four key features related to the synthetic conditions of BWO/MIL were filtered by the forward feature selection method based on support vector regression (SVR). Secondly, the SVR model with radical basis function for predicting the DR(RhB) of BWO/MIL was established with the key features and optimal hyperparameters. The correlation coefficients (R) between predicted and experimental DR(RhB) were 0.823 and 0.884 for leave-one-out cross-validation (LOOCV) and the external test, respectively. Thirdly, potential BWO/MIL nanocomposites with higher DR(RhB) were discovered by inverse projection, the prediction model, and virtual screening from the synthesis space. Meanwhile, an online web service (http://1.14.49.110/online_predict/BWO2) was built to share the model for predicting the DR(RhB) of BWO/MIL. Moreover, sensitivity analysis was brought into boosting the model's explainability and illustrating how the DR(RhB) of BWO/MIL changes over the four key features, respectively. The method mentioned here can provide valuable clues to develop new nanocomposites with the desired properties and accelerate the design of nano-photocatalysts. RSC 2023-06-06 /pmc/articles/PMC10408574/ /pubmed/37560433 http://dx.doi.org/10.1039/d3na00122a Text en This journal is © The Royal Society of Chemistry https://creativecommons.org/licenses/by-nc/3.0/ |
spellingShingle | Chemistry Zhai, Xiuyun Chen, Mingtong A machine learning-based nano-photocatalyst module for accelerating the design of Bi(2)WO(6)/MIL-53(Al) nanocomposites with enhanced photocatalytic activity |
title | A machine learning-based nano-photocatalyst module for accelerating the design of Bi(2)WO(6)/MIL-53(Al) nanocomposites with enhanced photocatalytic activity |
title_full | A machine learning-based nano-photocatalyst module for accelerating the design of Bi(2)WO(6)/MIL-53(Al) nanocomposites with enhanced photocatalytic activity |
title_fullStr | A machine learning-based nano-photocatalyst module for accelerating the design of Bi(2)WO(6)/MIL-53(Al) nanocomposites with enhanced photocatalytic activity |
title_full_unstemmed | A machine learning-based nano-photocatalyst module for accelerating the design of Bi(2)WO(6)/MIL-53(Al) nanocomposites with enhanced photocatalytic activity |
title_short | A machine learning-based nano-photocatalyst module for accelerating the design of Bi(2)WO(6)/MIL-53(Al) nanocomposites with enhanced photocatalytic activity |
title_sort | machine learning-based nano-photocatalyst module for accelerating the design of bi(2)wo(6)/mil-53(al) nanocomposites with enhanced photocatalytic activity |
topic | Chemistry |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10408574/ https://www.ncbi.nlm.nih.gov/pubmed/37560433 http://dx.doi.org/10.1039/d3na00122a |
work_keys_str_mv | AT zhaixiuyun amachinelearningbasednanophotocatalystmoduleforacceleratingthedesignofbi2wo6mil53alnanocompositeswithenhancedphotocatalyticactivity AT chenmingtong amachinelearningbasednanophotocatalystmoduleforacceleratingthedesignofbi2wo6mil53alnanocompositeswithenhancedphotocatalyticactivity AT zhaixiuyun machinelearningbasednanophotocatalystmoduleforacceleratingthedesignofbi2wo6mil53alnanocompositeswithenhancedphotocatalyticactivity AT chenmingtong machinelearningbasednanophotocatalystmoduleforacceleratingthedesignofbi2wo6mil53alnanocompositeswithenhancedphotocatalyticactivity |