Green Waste-to-Catalyst Inverse Design Using Generative Models

Mobinikhaledi et al. (2024) show the promise of repurposing waste (like eggshell-derived CaO) as green catalysts, but the process is largely empirical. I propose an inverse design workflow: start with a target reaction, then use generative models (e.g., diffusion models or GANs) trained on both compositional and performance data of green waste-derived catalysts to propose novel formulations with optimized activity/selectivity. This not only scales up the sustainable catalyst discovery pipeline but also applies cutting-edge ML techniques from materials science (see Jacobs et al., 2024; Hashimoto et al., 2025) to design eco-friendly catalysts with targeted properties. The resulting approach could revolutionize the use of abundant, low-cost waste streams in sustainable chemistry.

References:

1. Synthesis of Some Novel bis-Dihydrobenzochromenes Using CaO Nanoparticles Prepared from Eggshell as a Green and Natural Catalyst. A. Mobinikhaledi, Mahdia Hamidinasab, Najmieh Ahadi, Fatemeh Moghadasi, Parastoo Dehmolaei (2024). Organic Preparations and Procedures International.
2. Machine learning materials properties with accurate predictions, uncertainty estimates, domain guidance, and persistent online accessibility. R. Jacobs, Lane E. Schultz, Aristana Scourtas, K. Schmidt, Owen Price-Skelly, Will Engler, Ian T. Foster, B. Blaiszik, P. Voyles, Dane Morgan (2024). Machine Learning: Science and Technology.
3. A materials map integrating experimental and computational data via graph-based machine learning for enhanced materials discovery. Yusuke Hashimoto, Xue Jia, Hao Li, Takaaki Tomai (2025). APL Machine Learning.