In a SLAC-led study, four laboratories collaborated to ensure reproducibility in AI training data to predict new catalysts for future fuel production.
To turn abundant carbon dioxide into valuable fuel, we need a fast and efficient way of determining which catalysts work best over the longest time.
AI models have the potential to help guide catalyst selection, but as with internet chatbots, AI models are only as good as the data you put into them.
Speeding Up Catalyst Development – With AI Related Stories How AI Can Be Harnessed to Support Clean Energy TransitionHow AI Is Transforming the Electrification IndustryDoes the Positive Impact of AI Outweigh Its Environmental Costs?
AI models need large amounts of high-quality data for training.
In a SLAC-led study, four laboratories collaborated to ensure reproducibility in AI training data to predict new catalysts for future fuel production.
To turn abundant carbon dioxide into valuable fuel, we need a fast and efficient way of determining which catalysts work best over the longest time. AI models have the potential to help guide catalyst selection, but as with internet chatbots, AI models are only as good as the data you put into them.
By convening four laboratories from across the nation to test an experimental carbon monoxide-producing catalyst, a key first step in turning carbon dioxide into fuels, researchers at SLAC National Accelerator Laboratory have demonstrated the importance of generating highly reproducible experimental data when building AI models for investigations in science. They published the results in Nature Catalysis.
“Our findings are a reminder to exercise caution about what information we feed into a machine-learning model, and how the consistency of experimental data can influence the reliability of the outcomes,” said Selin Bac, a postdoctoral researcher at the University of California, Santa Barbara, and first author on the study.
Speeding Up Catalyst Development – With AI Related Stories How AI Can Be Harnessed to Support Clean Energy Transition
How AI Is Transforming the Electrification Industry
Does the Positive Impact of AI Outweigh Its Environmental Costs? With a good AI model, researchers can enter conditions such as temperature, length of time of the reaction, and catalyst formulation, then run the simulation and see a prediction of how well the catalyst performs. They can then confirm the predictions with a few well-designed experiments, ultimately speeding up catalyst discovery and implementation at a global scale. In addition to saving time and money, such models can also explore conditions that are difficult to achieve in the lab. Most lab catalysis studies can only look at short time periods (days), but catalyst deactivation occurs over time (months to years) due to buildup of impurities and repeated exposure to high temperatures. AI models need large amounts of high-quality data for training. To generate the data, the four labs performed a set of round-robin experiments, in which multiple laboratories conduct the same tests to evaluate reproducibility using previously agreed upon protocols and the same rhodium-based catalyst.
