Designing durable, sustainable, high-performance materials for clean energy infrastructure
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Civilization and modern societies would not be possible without manmade materials. Considering their production volumes, their supporting role in nearly all industrial processes, and the impact of their sourcing and production on the environment, metals and alloys are and will be of prominent importance for the clean energy transition. The focus of materials discovery must move to more specialized, application-tailored green alloys that outperform the legacy materials not only in performance but also in sustainability and resource efficiency. This white paper summarizes a joint Canadian-German initiative aimed at developing a materials acceleration platform (MAP) focusing on the discovery of new alloy families that will address this challenge. We call our initiative the “Build to Last Materials Acceleration Platform” (B2L-MAP) and present in this perspective our concept of a three-tiered self-driving laboratory that is composed of a simulation-aided pre-selection module (B2L-select), an artificial intelligence (AI)-driven experimental lead generator (B2L-explore), and an upscaling module for durability assessment (B2L-assess). The resulting tool will be used to identify and subsequently demonstrate novel corrosion-resistant alloys at scale for three key applications of critical importance to an offshore, wind-driven hydrogen plant (reusable electrical contacts, offshore infrastructure, and oxygen evolution reaction catalysts).
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it