Materials discovery of ion-selective membranes using artificial intelligence

Area of Science:

• Materials Science
• Computational Chemistry
• Chemical Engineering

Background:

• Traditional methods for producing ion-selective membranes (ISMs) face limitations, including high experimental costs and time-consuming computations.
• There is a need for more efficient and cost-effective approaches in ISM production and materials discovery.

Purpose of the Study:

• To review the pivotal role of artificial intelligence (AI) in advancing materials discovery and engineering of ion-selective membranes (ISMs).
• To explore how AI can overcome the limitations of traditional experimental methods in ISM development.

Main Methods:

• Utilizing AI for data analysis to minimize experimental testing requirements.
• Accelerating computational methods through AI-driven models based on ISM simulations.
• Coupling AI with computational chemistry to incorporate atomic features into predictive models.

Main Results:

• AI acts as a crucial bridge between experimental data and computational chemistry, enabling the development of sophisticated predictive models.
• Hybrid AI and computational chemistry approaches can effectively utilize both experimental data and atomic properties for materials discovery.
• AI-based materials discovery facilitates the investigation of membranes for ion extraction.

Conclusions:

• AI-powered materials discovery presents a transformative approach for engineering advanced ion-selective membranes (ISMs).
• This methodology offers significant potential for overcoming current challenges and paving the way for future innovations in ISM development and ion extraction applications.