The Open Catalyst Project is a collaborative research effort between
Fundamental AI Research (FAIR) at Meta and Carnegie Mellon University's (CMU)
Department of Chemical Engineering. The aim is to use AI to
model and discover new catalysts for use in renewable energy storage
to help in addressing climate change.
Scalable and cost-effective solutions to renewable energy
storage are essential to addressing the world's rising energy needs
while reducing climate change. As we increase our reliance on
renewable energy sources such as wind and solar, which produce
intermittent power, storage is needed to transfer power from times of
peak generation to peak demand. This may require the storage of power
for hours, days, or months. One solution that offers the potential of
scaling to nation-sized grids is the conversion of renewable energy to
other fuels, such as hydrogen. To be widely adopted, this process
requires cost-effective solutions to running chemical reactions.
An open challenge is finding low-cost catalysts to drive these
reactions at high rates. Through the use of quantum mechanical
simulations (density functional theory), new catalyst structures can
be tested and evaluated. Unfortunately, the high computational cost
of these simulations limits the number of structures that may be
tested. The use of AI or machine learning may provide a method to
efficiently approximate these calculations, leading to new
approaches in finding effective catalysts.
To enable the broader research community to participate in this
important project, we have released the
Open Catalyst 2020
(OC20) and 2022 (OC22) datasets
for training ML models. These datasets altogether contain 1.3 million molecular
relaxations with results from over 260 million DFT calculations. In addition to
the data, baseline models and code are open-sourced on
our Github page.
View the leaderboard to see the latest results and to submit your own to the evaluation server! Join the discuss forum to join the discussion with the community
and ask any questions.
Download our research papers that describe baselines, evaluation
metrics, and the dataset.
Relaxation trajectory of a carboxylic group (CO*) adsorbed on top of a Zirconium atom coming from a
given Zr3Sc
crystallographic orientation. The above mechanism is an important building block for CO2
reduction and fuel generation.
mp-865362
Relaxation trajectory of a bidentate C2-based group (OHCHCH2OH**), a possible
CO2 reduction intermediate, adsorbed on
top of Zinc and Iridium atoms coming from a given Zn3Ir crystallographic orientation.
mp-567397*
Relaxation trajectory of a Dimethylhydrazine moiety ((CH3)2NNH2*), commonly
used as hypergolic rocket fuel, adsorbed on
top of a Tungsten atom coming from a given W2C crystallographic orientation.
mp-213
Relaxation trajectory of an Ammonia group (NH3*) adsorbed on top of a Palladium atom
coming from a given CaPd
crystallographic orientation. Ammonia is critical in the production of agricultural fertilizers. The
discovery of
efficient catalysts plays an important role towards the advancement of sustainable argiculture.
mp-862300
Relaxation trajectory of a Hydroxyl group (OH*) adsorbed on top of a Scandium atom coming from a
given Sc2GaOs
crystallographic orientation. OH* is involved in oxygen evolution and reduction (OER/ORR) reactions,
important steps in
the development of effective fuel cells.
mp-112
Relaxation trajectory of a bidentate C2-based group (CCH3*), a possible
CO2 reduction intermediate, adsorbed on top of
an Yttrium atom from a given Y(hc) crystallographic orientation.
