Reproducible Sorbent Materials Foundry for Carbon Capture at Scale

TL;DR

This paper proposes an autonomous foundry for rapid evaluation of sorbent materials, especially metal organic frameworks, to accelerate the development of scalable carbon capture solutions using automated data collection and machine learning.

Contribution

It introduces a hierarchical, automated sorbent materials foundry that integrates synthesis, processing, and performance data for machine learning-driven design.

Findings

Enables rapid, automated evaluation of sorbent materials.

Facilitates end-to-end data collection for machine learning.

Aims to accelerate development of scalable carbon capture materials.

Abstract

We envision an autonomous sorbent materials foundry (SMF) for rapidly evaluating materials for direct air capture of carbon dioxide (CO2), specifically targeting novel metal organic framework materials. Our proposed SMF is hierarchical, simultaneously addressing the most critical gaps in the inter-related space of sorbent material synthesis, processing, properties, and performance. The ability to collect these critical data streams in an agile, coordinated, and automated fashion will enable efficient end-to-end sorbent materials design through machine learning driven research framework.