Nanoporous Material Recognition via 3D Convolutional Neural Networks: Prediction of Adsorption Properties
Journal
Journal of Physical Chemistry Letters
Journal Volume
12
Journal Issue
9
Pages
2279-2285
Date Issued
2021
Author(s)
Abstract
Nanoporous materials can be effective adsorbents for various energy applications. Because of their abundant number, brute-force-based material discovery can, however, be challenging. Data-driven approaches can be advantageous for such purposes. In this study, we demonstrate for the first time the applicability of a 3D convolutional neural network (CNN) in material recognition for predicting adsorption properties. 2D CNNs have been widely applied to image recognition, where the CNN self-learns important features of images, without the need of handcrafting features that are subject to human bias. This study explores methane adsorption in zeolites as a case study, where ?6500 hypothetical zeolites are utilized to train/validate our designed CNN model. The CNN model offers highly accurate predictions, and the self-learned features resemble the channel and pore-like geometry of structures. This study demonstrates the extension of computer vision to materials science and paves the way for future studies such as carbon capture. ?
Subjects
Adsorption
Convolution
Forecasting
Image recognition
Nanopores
Plasma interactions
Porous materials
Zeolites
Adsorption properties
Data-driven approach
Energy applications
Highly accurate
Important features
Material recognition
Methane adsorption
Nano-porous materials
Convolutional neural networks
Type
journal article