Membrane Science Meets Machine Learning: Future and Potential Use in Assisting Membrane Material Design and Fabrication

ABSTRACT

The evolving membrane technology integrated with machine learning (ML) algorithms can significantly advance the novel membrane material design and fabrication. Although several studies have reported ML-based assistance in membrane development, none of them have offered a complete analysis of existing ML-assisted membrane fabrication methods from material design perspective. A two-way information gateway is therefore necessary to achieve the desired objective, whereby experienced researchers and data scientists from both sides need to provide valuable insights into novel membrane development process. In this work, we offer a midway platform by providing an overall view and scopes of ML uses in membrane science. This is accomplished by analyzing reported ML-assisted membrane fabrications via lensing through the overall ML development. This work culminates in identifying four crucial factors affecting ML-assisted membrane development: data mining, material functional description, selection of ML models and model interpretation. A future direction is proposed by making specific ML models and descriptors suggestions, in addition to molecular similarity analysis technique and ML-based image processing. We believe the proposed approaches and analyses through our identified lens will prove crucial for the future of ML-assisted membrane material design and development.

KEYWORDS:

• Machine learning
• membrane material
• membrane development
• artificial intelligence

Acknowledgments

The presented work was supported in-part by the City of Grand Forks grant offered to Dr Ali Alshami through Project number: UND0023658.

DISCLOSURE STATEMENT

No potential conflict of interest was reported by the authors.

AUTHORS RESPONSIBILITIES

Musabbir Talukder: Writing – Original Draft, Writing – Review & Editing; Ali Alshami: Conceptualization Writing – Review & Editing, Supervision; Arash Tayyebi: Writing – Review & Editing; Nadhem Ismail: Writing – Review & Editing; Xue Yu: Writing – Review & Editing.

Additional information

Funding

The work was supported by the North Dakota Industrial Commission [UND0026282]; City of Grand Forks Grant [UND0023658].