Toward artificial intelligence and machine learning-enabled frameworks for improved predictions of lifecycle environmental impacts of functional materials and devices
Bibliographic record
Abstract
Abstract The application of functional materials and devices (FM&Ds) underpins numerous products and services, facilitating improved quality of life, but also constitutes a huge environmental burden on the natural ecosystem, prompting the need to quantify their value-chain impact using the bottom-up life cycle assessment (LCA) framework. As the volume of FM&Ds manufactured increases, the LCA calculation speed is constrained due to the time-consuming nature of data collection and processing. Moreover, the bottom-up LCA framework is limited in scope, being typically static or retrospective, and laced with data gap challenges, resulting in the use of proxy values, thus limiting the relevance, accuracy, and quality of results. In this prospective article, we explore how these challenges across all phases of the bottom-up LCA framework can be overcome by harnessing new insights garnered from computationally guided parameterized models enabled by artificial intelligence (AI) methods, such as machine learning (ML), applicable to all products in general and specifically to FM&Ds, for which adoption remains underexplored. Graphical abstract
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".