To solve this problem, we need a large and comprehensive dataset

• Relevant datasets for catalysts in renewable energy storage are limited
• An ideal dataset for this purpose would be large scale and contain DFT simulations of relevant catalysts and adsorbates
• The dataset should allow the training of models that take rough initial atomic positions as input and compute relaxed geometries and energies
• The majority of DFT calculations are spent computing relaxed geometries, so efficient approximations of this process are needed

hypothesis

Efficient approximations of DFT calculations could enable large-scale exploration of new catalysts for renewable energy storage. There are over 10,000 possible combinations of up to three metals from a pool of 40, and the number of possibilities increases indefinitely due to the various ratios and configurations that can be used. To determine whether a potential catalyst is effective, it must be simulated in interaction with all potential adsorbates at all possible surfaces and binding sites, resulting in thousands of simulations per catalyst. This search space contains millions of possibilities requiring billions of simulations to explore. However, with efficient DFT approximations, it may be possible to compute all of them through brute force and verify the most promising candidates through traditional DFT calculations before further exploration into their feasibility.

implications

• Efficient DFT approximations could be applied to various high-impact problems
• Examples include methanation (reaction of CO2 and hydrogen to form methane and water) and the production of ammonia fertilizer
• Methanation could result in a high density, carbon-neutral fuel that fits into current energy infrastructure
• The production of ammonia, a key ingredient in agricultural fertilizer, currently accounts for about 1% of CO2 emissions
• Emissions could be reduced by using renewable energy to produce input hydrogen and by finding a more efficient reaction for the Haber-Bosch process.

the case for renewable energy storage

design of electrocatalysts

classes of materials for electrocatalysts