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Identifier |
000463155 |
Title |
From potential energy surface to gas adsorption via deep learning |
Alternative Title |
Από τη δυναμική ενεργειακή επιφάνεια στην προσρόφηση αερίων μέσω βαθιάς μάθησης |
Author
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Σαρικάς, Αντώνιος Π.
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Thesis advisor
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Φρουδάκης, Γεώργιος
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Reviewer
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Τρικαλίτης, Παντελής
Νεοχωρίτης, Κωνσταντίνος
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Abstract |
Μetal-organic frameworks, or in short MOFs, thanks to their ultra high poros-
ity and surface area, are deemed as prominent candidates for applications involving gas
adsorption. However, their intrinsic combinatorial nature translates to a practically
infinite material space, rendering the identification of novel materials with traditional
methods cumbersome. Over the last years, machine learning approaches based on pre-
dictive models have been developed, allowing researchers to rapidly screen large databases of MOFs.
The quality of these models is highly dependent on the mathematical representation of a material, thus
necessitating the use of informative inputs. In this thesis, we propose a generalized framework for pre-
dicting gas adsorption properties, using as one and only input the potential energy surface. We treat
the latter as a 3D energy image and then pass it through a 3D convolutional neural network, known
for its ability to process image-like data. The proposed pipeline is applied in MOFs for predicting CO2
uptake. The resulting model outperforms both in terms of accuracy and data efficiency a conventional
one built upon textual properties. Additionally, we demonstrate the transferability of the approach
to other host-guest systems, by examining CH4 uptake in covalent organic frameworks. The perfor-
mance and generality of the proposed approach along with the fast input calculation thanks to paral-
lelization, renders it suitable for large scale screening. Finally, discussion for improving and extending
the suggested scheme is provided.
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Language |
English |
Subject |
Machine learning |
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Porous materials |
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Μηχανική μάθηση |
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Πορώδη υλικά |
Issue date |
2024-03-21 |
Collection
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School/Department--School of Sciences and Engineering--Department of Chemistry--Post-graduate theses
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Type of Work--Post-graduate theses
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Permanent Link |
https://elocus.lib.uoc.gr//dlib/d/8/2/metadata-dlib-1710230106-257733-10115.tkl
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Views |
80 |