PACMAN: A Robust Partial Atomic Charge Predicter for Nanoporous Materials Based on Crystal Graph Convolution Networks-Reference-Cited by-同舟云学术

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PACMAN: A Robust Partial Atomic Charge Predicter for Nanoporous Materials Based on Crystal Graph Convolution Networks

Published:2024-06-01 Issue:12 Volume:20 Page:5368-5380
ISSN:1549-9618
Container-title:Journal of Chemical Theory and Computation
language:en
Short-container-title:J. Chem. Theory Comput.

Author:

Zhao Guobin¹^ORCID,Chung Yongchul G.¹^ORCID

Affiliation:

1. School of Chemical Engineering, Pusan National University, Busan 46241, South Korea

Funder

National Research Foundation of Korea

National Supercomputing Center, Korea Institute of Science and Technology Information

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jctc.4c00434

Reference89 articles.

1. Secondary building units as the turning point in the development of the reticular chemistry of MOFs

2. A reticular chemistry guide for the design of periodic solids

3. Beyond post-synthesis modification: evolution of metal–organic frameworks via building block replacement

4. Mixed component metal-organic frameworks: Heterogeneity and complexity at the service of application performances

5. Advances, Updates, and Analytics for the Computation-Ready, Experimental Metal–Organic Framework Database: CoRE MOF 2019

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1. Theoretical Study of CH₄ and CO₂ Separation by IRMOFs;ACS Omega;2024-09-04

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