Design of functional and sustainable polymers assisted by artificial intelligence

H Tran, R Gurnani, C Kim, G Pilania, HK Kwon… - Nature Reviews …, 2024 - nature.com
Artificial intelligence (AI)-based methods continue to make inroads into accelerated
materials design and development. Here, we review AI-enabled advances made in the …

Leveraging machine learning in porous media

M Delpisheh, B Ebrahimpour, A Fattahi… - Journal of Materials …, 2024 - pubs.rsc.org
The emergence of artificial intelligence (AI) and, more particularly, machine learning (ML),
has had a significant impact on engineering and the fundamental sciences, resulting in …

Machine learning enables interpretable discovery of innovative polymers for gas separation membranes

J Yang, L Tao, J He, JR McCutcheon, Y Li - Science Advances, 2022 - science.org
Polymer membranes perform innumerable separations with far-reaching environmental
implications. Despite decades of research, design of new membrane materials remains a …

Machine learning for the advancement of membrane science and technology: A critical review

G Ignacz, L Bader, AK Beke, Y Ghunaim… - Journal of Membrane …, 2024 - Elsevier
Abstract Machine learning (ML) has been rapidly transforming the landscape of natural
sciences and has the potential to revolutionize the process of data analysis and hypothesis …

AI for nanomaterials development in clean energy and carbon capture, utilization and storage (CCUS)

H Chen, Y Zheng, J Li, L Li, X Wang - ACS nano, 2023 - ACS Publications
Zero-carbon energy and negative emission technologies are crucial for achieving a carbon
neutral future, and nanomaterials have played critical roles in advancing such technologies …

Gas permeability, diffusivity, and solubility in polymers: Simulation-experiment data fusion and multi-task machine learning

BK Phan, KH Shen, R Gurnani, H Tran… - npj Computational …, 2024 - nature.com
Abstract Machine learning (ML) models for predicting gas permeability through polymers
have traditionally relied on experimental data. While these models exhibit robustness within …

[HTML][HTML] Multi-scale design of MOF-based membrane separation for CO2/CH4 mixture via integration of molecular simulation, machine learning and process modeling …

X Cheng, Y Liao, Z Lei, J Li, X Fan, X **: Ready for prime time
J Wang, K Tian, D Li, M Chen, X Feng, Y Zhang… - Separation and …, 2023 - Elsevier
Membrane technology is a promising next-generation gas separation technology and has
drawn tremendous research interest during the past decades. Despite the advanced …

[HTML][HTML] Machine learning for membrane design and discovery

H Yin, M Xu, Z Luo, X Bi, J Li, S Zhang… - Green Energy & …, 2024 - Elsevier
Membrane technologies are becoming increasingly versatile and helpful today for
sustainable development. Machine Learning (ML), an essential branch of artificial …

[HTML][HTML] Mixed-matrix membranes containing porous materials for gas separation: from metal–organic frameworks to discrete molecular cages

Z Yang, Z Wu, SB Peh, Y Ying, H Yang, D Zhao - Engineering, 2023 - Elsevier
Abstract Mixed-matrix membranes (MMMs), which combine porous materials with a
polymeric matrix, have gained considerable research interest in the field of gas separation …