Modelling of framework materials at multiple scales: current practices and open questions.

Area of Science:

• Materials Science
• Computational Chemistry
• Solid-State Physics

Background:

• Framework materials have seen significant research growth over the past decade.
• Both experimental and computational approaches are crucial for studying these materials.

Purpose of the Study:

• To review current multi-scale modeling methodologies for framework materials.
• To highlight recent advancements and identify open challenges in the field.

Main Methods:

• Review of atomistic simulation techniques.
• Discussion of material database development.
• Exploration of machine learning applications for property prediction.

Main Results:

• Advances in atomistic simulations provide detailed insights into framework material behavior.
• Material databases facilitate efficient data management and retrieval.
• Machine learning models show promise for accelerating the prediction of material properties.

Conclusions:

• Multi-scale modeling is essential for understanding complex framework materials.
• Continued development in computational methods and data science is key to future discoveries.
• Addressing current challenges will unlock new applications for framework materials.