To identify promising catalysts, research scientists use quantum mechanical simulation tools like density functional theory (DFT) to predict adsorption energies of small molecules on potential catalysts. DFT uses quantum mechanics to simulate the movement of atoms in a given scenario, iteratively moving the positions of atoms in the system until they reach their lowest energy configuration, also known as a relaxation. The issue is that each DFT relaxation takes hundreds of hours to complete on a multicore machine ... although faster, larger machines speed this up ... the search is at to continue to be more resource intensive than it needs to be for just an informed search which narrows the range of alternatives for a full ab initio simulation of properties.
To identify promising catalysts, research scientists use quantum mechanical simulation tools like density functional theory (DFT) to predict adsorption energies of small molecules on potential catalysts. DFT uses quantum mechanics to simulate the movement of atoms in a given scenario, iteratively moving the positions of atoms in the system until they reach their lowest energy configuration, also known as a relaxation. The issue is that each DFT relaxation takes hundreds of hours to complete on a multicore machine ... although faster, larger machines speed this up ... the search is at to continue to be more resource intensive than it needs to be for just an informed search which narrows the range of alternatives for a full ab initio simulation of properties.
The folks at OpenCatalyst are among those who think that perhaps ML can accelerate this process — OC22 Oxygen Evolution Reaction (OER) replace DFT simulations that currently take hours or days with ML predictions that take a few seconds.