From biomass waste to CO2 capture: a multi-fidelity machine learning workflow for high-throughput screening of activated carbons
Bibliographic record
Abstract
Rising atmospheric CO 2 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 2 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 2 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".