Background: Building the next generation of batteries (efficient, compact, and sustainable) depends on finding new materials with just the right properties. That’s where metal-organic frameworks (MOFs) come in—a class of crystalline, porous materials that can also be electrically conductive. They’ve gotten a lot of attention as a promising option for battery electrodes. But finding the best MOFs is like searching for a needle in a haystack. That’s why a data-driven approach can help us speed up this discovery process.
Goal: Use data science techniques to predict key properties of MOFs, like their band gaps, to identify promising candidates for future energy storage devices like next-gen batteries.
Data:
• QMOF Database: ~20,000+
Method/s:
• Regression Analysis