Machine learning-assisted design of porous carbons for removing paracetamol from aqueous solutions-Reference-Cited by-同舟云学术

Machine learning-assisted design of porous carbons for removing paracetamol from aqueous solutions

Published:2022-10 Issue: Volume:198 Page:371-381
ISSN:0008-6223
Container-title:Carbon
language:en
Short-container-title:Carbon

Author:

Kowalczyk Piotr^ORCID,Terzyk Artur P.^ORCID,Erwardt Paulina^ORCID,Hough Michael,Deditius Artur P.^ORCID,Gauden Piotr A.,Neimark Alexander V.,Kaneko Katsumi^ORCID

Funder

Poznańskie Centrum Superkomputerowo-Sieciowe

Publisher

Elsevier BV

Subject

General Chemistry,General Materials Science

Reference38 articles.

1. Carbon science perspective in 2020: current research and future challenges;Bianco;Carbon,2020

2. Accelerated discoveries of mechanical properties of graphene using machine learning and high-throughput computation;Zhang;Carbon,2019

3. Characterizing fracture stress of defective graphene samples using shallow and deep artificial neural networks;Dewapriya;Carbon,2020

4. Machine Learning for nanomaterial toxicity risk assessment;Gernand;IEEE Intell. Syst.,2014

5. Machine-learning-assisted fabrication: Bayesian optimization of laser-induced graphene patterning using in-situ Raman analysis

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