A High Throughput Experimentation & Material Informatics Platform for the Discovery of Molten Salt Reactor Candidate Structural Materials
Bibliographic record
Abstract
ASME Sec(III) Div(5) code-certified structural alloys for Molten (halide) Salt Reactors (MSRs) such as 800H, SS316, and IN617 have significant Cr content. This makes them readily-degradable in molten halide salts due to the thermodynamic favorability of soluble chromium halide formation. It is therefore imperative to discover alloys with corrosion resistance that exceeds those of currently-certified alloys, which also possess the necessary high hardness and irradiation-resistance at reactor operating temperatures as a prerequisite for the licensing and deployment of MSRs. The largely-unexplored quasi-infinite quarternary FeCrMnNi High Entropy Alloy space shows promise to yield desired alloys. We present a high-throughput (HTP) process demonstrating a turnaround time from fabrication to corrosion-testing and analysis of 1 week for 25 samples. 70 alloy samples of 1cm2 were each corrosion-tested on 0.3g salt droplets in isolated corrosion environments. The HTP platform includes the development of an in-situ high-temperature electrochemical sensor system capable of automating the analysis of dissolved corrosion product analytes in the salt. The results of the 70 corrosion tests were used to train a Machine Learning model to predict corrosion performance metrics (eg. Elemental corrosion concentration into salt) based on an input vector parametrizing the physical properties (“descriptors”) of the alloys in the sample set. The model was tested on 20 additional samples and demonstrated conservative predictive capability as well as facilitated the reduction of input feature space from 62 dimensions to 4 dimensions with negligible loss in predictive accuracy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.006 | 0.003 |
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".