New neural networks calculate catalysts’ adsorption energy ‘with lightning-fast speed’

A novel neural network can help chemists calculate adsorption energies effectively and efficiently – up to one million times faster than state-of-the-art methodologies. Once again, artificial intelligence has proven to be a potent tool to accelerate discovery, particularly in the field of heterogeneous catalysis. The improvements in the design and development of catalysts could create cleaner chemical processes, and enable the exploration of innovative raw materials such as biomass and plastics.

‘[Our neural network is] a catalyst for heterogeneous catalysis,’ says Núria López from ICIQ in Tarragona, Spain, who co-led the study. Traditionally, computational chemists calculate adsorption energies with density functional theory (DFT), which have several limitations. ‘DFT is really time consuming, plus it doesn’t really work for very large molecules,’ she adds. ‘The new neural network algorithms predict the adsorption energy of big molecules on metal surfaces orders of magnitude faster,’ explains López. In heterogeneous catalysis, activity is actually associated with adsorption energy. Eventually, estimating the energy of adsorption accurately could lead to more efficient experiments in the lab.