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<p>Rising atmospheric CO<sub>2</sub> 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 CO<sub>2</sub> 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 CO<sub>2</sub> 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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月24日

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  • Faezeh Hajiali,
  • Naoko Ellis,
  • Bhushan Gopaluni

摘要

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.