LAUSR

Catalytic upgrading of biowaste to chemicals and fuels is a key step towards circular economy. Computational investigations of catalytic upgrading of biowaste have made valuable contributions towards catalysts design. Recent applications of 1) Density Functional Theory (DFT) and molecular dynamics (MD) simulations, and microkinetic modelling to derive insights on active sites, reactions mechanisms, solvation and catalyst deactivation, 2) DFT and MD simulations, multiscale models, and statistical methods for identification of structure of catalyst nanoparticles, 3) DFT calculations and statistical methods for identification of catalytic descriptors and development of scaling relations, are discussed. A recent trend is to exploit advances in data science and data repositories for catalysis. There is immense potential for combined multiscale computational techniques and machine learning to enable in silico design of catalysts for the upgradation of biowaste.