Design of Amine-Functionalized Materials for Direct Air Capture Using Integrated High-Throughput Calculations and Machine Learning

TL;DR

This study combines machine learning and high-throughput modeling to rapidly identify novel amine-functionalized materials with high CO2 binding capacity for direct air capture, advancing scalable climate mitigation technologies.

Contribution

It introduces a new ML-based screening method that predicts CO2 binding energies and assesses synthesizability, enabling rapid discovery of over 2,400 viable DAC materials.

Findings

Successfully screened over 1.6 million molecules.

Identified nearly 2,500 synthesizable DAC materials.

Demonstrated high accuracy of ML predictions for CO2 binding energies.

Abstract

Direct air capture (DAC) of carbon dioxide is a critical technology for mitigating climate change, but current materials face limitations in efficiency and scalability. We discover novel DAC materials using a combined machine learning (ML) and high-throughput atomistic modeling approach. Our ML model accurately predicts high-quality, density functional theory-computed CO$_{2}$ binding enthalpies for a wide range of nitrogen-bearing moieties. Leveraging this model, we rapidly screen over 1.6 million binding sites from a comprehensive database of theoretically feasible molecules to identify materials with superior CO$_{2}$ binding properties. Additionally, we assess the synthesizability and experimental feasibility of these structures using established ML metrics, discovering nearly 2,500 novel materials suitable for integration into DAC devices. Altogether, our high-fidelity database and ML framework represent a significant advancement in the rational development of scalable, cost-effective carbon dioxide capture technologies, offering a promising pathway to meet key targets in the global initiative to combat climate change.