Machine Learning in Membrane Design: From Property Prediction to AI-Guided Optimization-Reference-Cited by-同舟云学术

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Machine Learning in Membrane Design: From Property Prediction to AI-Guided Optimization

Published:2024-03-04 Issue:10 Volume:24 Page:2953-2960
ISSN:1530-6984
Container-title:Nano Letters
language:en
Short-container-title:Nano Lett.

Author:

Cao Zhonglin¹^ORCID,Barati Farimani Omid¹,Ock Janghoon²^ORCID,Barati Farimani Amir¹²³^ORCID

Affiliation:

1. Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh Pennsylvania 15213, United States

2. Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh Pennsylvania 15213, United States

3. Machine Learning Department, Carnegie Mellon University, Pittsburgh Pennsylvania 15213, United States

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.nanolett.3c05137

Reference50 articles.

1. Seven chemical separations to change the world

2. Carbon Dioxide Separation with a Two-Dimensional Polymer Membrane

3. Measurement of the Elastic Properties and Intrinsic Strength of Monolayer Graphene

4. Water desalination with a single-layer MoS2 nanopore

5. Water Desalination across Nanoporous Graphene

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1. Machine learning for the advancement of membrane science and technology: A critical review;Journal of Membrane Science;2025-01

2. 2D Carbonaceous Materials for Molecular Transport and Functional Interfaces: Simulations and Insights;Accounts of Chemical Research;2024-08-27

3. A practical machine learning approach for predicting the quality of 3D (bio)printed scaffolds;Biofabrication;2024-07-25

4. Fast Water Desalination with a Graphene–MoS₂ Nanoporous Heterostructure;ACS Applied Materials & Interfaces;2024-05-20

5. Combined Machine Learning and High-Throughput Calculations Predict Heyd–Scuseria–Ernzerhof Band Gap of 2D Materials and Potential MoSi₂N₄ Heterostructures;The Journal of Physical Chemistry Letters;2024-05-14

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