Machine Learning for Climate Change: Guiding Discovery of Sorbent Materials for Direct Air Capture of CO2 (Proposals Track)

Diana L Ortiz-Montalvo (NIST); Aaron Gilad Kusne (NIST); Austin McDannald (NIST); Daniel Siderius (NIST); Kamal Choudhary (NIST); Taner Yildirim (NIST)

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Carbon Capture & Sequestration Active Learning


The global climate crisis requires interdisciplinary collaboration. The same is true for making significant strides in materials discovery for direct air capture (DAC) of carbon dioxide (CO2). DAC is an emerging technology that captures CO2 directly from the atmosphere and it is part of the solution to achieving carbon neutrality by 2050. The proposed project is a collaborative effort that tackles climate change by using machine learning to guide scientists to novel, optimized, advanced sorbent materials for direct air capture of CO2. Immediate impacts will include high throughput machine learning tools for developing new, cost-effective CO2 sorption materials, and continued, expanded collaborations with potential domestic and international stakeholders.