Emerging Directions for Carbon Capture Technologies: A Synergy of High-Throughput Theoretical Calculations and Machine Learning

As the world grapples with the challenges of energy transition and industrial decarbonization, the development of carbon capture technologies presents a promising solution. The Scalable Modeling, Artificial Intelligence (AI), and Rapid Theoretical calculations, referred as SMART here, is an interdisciplinary approach that combines high-throughput calculation and data-driven modeling with expertise from chemical, materials, environmental, computer and data science and engineering, leading to the development of advanced capabilities in simulating and optimizing carbon capture processes. This perspective discusses the state-of-the-art material discovery research enabled by high-throughput calculation and data-driven modeling. Further, we propose a framework for material discovery, and illustrate the synergies among deep learning models, pretrained models, and comprehensive data sets, emerging as a robust framework for data-driven design and development in carbon capture. In essence, the adoption of the SMART approach promises a revolutionary impact on efforts in energy transition and industrial decarbonization.

Automated summary

This article discusses the application of the SMART approach in the development of carbon capture technologies, combining high-throughput calculation and data-driven modeling to achieve advanced capabilities in simulating and optimizing carbon capture processes. The authors also propose a framework for material discovery and emphasize the synergies among deep learning models, pretrained models, and comprehensive data sets.