Design of active sites for amine-functionalized direct air capture materials using integrated high-throughput calculations and machine learning.

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 active sites for 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 CO₂ 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 binding sites with superior CO₂ binding properties. Additionally, we assess the feasibility of experimentally synthesizing these structures using established ML metrics,

SUBMITTER: Davis MC 

PROVIDER: S-EPMC12780130 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature