Demir, Hakan

Profile Picture
Profile URL
https://hdl.handle.net/20.500.14411/10578
Name Variants
D., Hakan
Demir, H.
H., Demir
Demir, Hakan
Demir, H.
Hakan Demir
Job Title
Doktor Öğretim Üyesi
Email Address
hakan.demir@atilim.edu.tr
Main Affiliation
Chemical Engineering
Status
Current Staff
Website
ORCID ID
0000-0002-4285-5010
Scopus Author ID
55955529600
Turkish CoHE Profile ID
Google Scholar ID
6s8dusgAAAAJ
WoS Researcher ID
AAB-4746-2019

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1

Scopus Citation Count

1

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1.00

Scopus Citations per Publication

1.00

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Advanced Theory and Simulations1
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    Article
    Citation - WoS: 1
    Citation - Scopus: 1
    Effect of Atomic Charges on C2H2/Co2 Separation Performances of Covalent-Organic Framework Adsorbents
    (John Wiley and Sons Inc, 2025) Demir, H.; Erucar, I.
    A critical factor for the accuracy of computational screening studies is the method employed to assign atomic charges. While chemically meaningful atomic charges can be obtained using a quantum chemistry method-based charge assignment technique (density-derived electrostatic and chemical method (DDEC6)), its application to large material datasets remains computationally demanding. As an alternative, machine-learning (ML) models can offer the ability to determine atomic charges with high accuracy and speed. Herein, two ML models, Partial Atomic Charge Predicter for Porous Materials based on Graph Convolutional Neural Network (PACMAN) and Partial Atomic Charges in Metal-Organic Frameworks (PACMOF), are utilized to predict atomic charges in Clean, Uniform, Refined with Automatic Tracking from Experimental Database (CURATED) covalent-organic frameworks (COFs). The predicted atomic charges are used in simulations to assess COFs' C2H2/CO2/CH4 separation performances in comparison with reference DDEC6-based performances. Results show PACMAN charges can more effectively reproduce DDEC6-based charges and corresponding separation performance metrics, underscoring their suitability for high-throughput material screening. Additionally, the proportions of Coulombic interactions to van der Waals interactions are systematically analyzed, revealing substantial variation across both narrow and wide pores. This study highlights that ML models can be applied to obtain atomic charges that could enable attaining accurate material performance evaluations. © 2025 The Author(s). Advanced Theory and Simulations published by Wiley-VCH GmbH.