From biomass waste to CO2 capture: a multi-fidelity machine learning workflow for high-throughput screening of activated carbons.

Rising atmospheric CO2 levels threaten climate stability, demanding transformative solutions in carbon capture, utilization, and storage. Porous activated carbons (ACs) derived from sustainable waste sources offer a promising route for cost-effective and eco-friendly carbon capture, thanks to their tunable surface chemistry and high surface areas. However, optimizing ACs for peak CO2 uptake is often hindered by complex, resource-intensive experimental workflows and the scarcity of high-quality data. This study presents a machine learning-driven framework that combines a multi-headed one-dimensional convolutional neural network (MH1DCNN) with multi-fidelity Bayesian optimization (MFBO) to efficiently navigate large design spaces by balancing exploration of uncertain regions with exploitation of known high-performing candidates. The MH1DCNN captures nonlinear relationships between physicochemical properties and CO2 uptake, serving as a deployable low-fidelity model. Using 841 literature-reported samples as high-cost, high-fidelity data and MH1DCNN-generated predictions as low-cost, low-fidelity evaluations, MFBO fuses these information sources through a probabilistic surrogate model, enabling rapid and cost-effective optimization. This approach reduces high-fidelity evaluation requirements by over 75% and identifies top-performing candidates using only 13 high-fidelity acquisitions. This scalable, data-driven strategy supports the development of closed-loop experiment-analysis-planning systems for future autonomous laboratories and accelerates sustainable materials discovery.