Achieving sustainability goals will require the use of feedstocks such as plastics and biomass instead of oil. Moreover, industries are constantly searching for more efficient and sustainable processes, which often require precise simulations. In the field of heterogeneous catalysis, determining the energy of molecules adsorbed on surfaces is crucial to estimate catalyst performance, which is typically done using density functional theory (DFT). However, for large organic molecules like those in plastics and biomass, this cannot be done.
Click here for original story, A graph neural network for fast evaluation of the adsorption energy in heterogeneous catalysis
Source: Phys.org