Engineering Digital Twins for AI-Assisted Scientific Discovery: Case of Plasma-Enhanced Deposition
Bibliographic record
Abstract
Scientific discovery increasingly demands methods that can cope with complex systems and high-dimensional experimental spaces. Traditional iterative approaches—rooted in hypothesis, experimentation, and modeling—struggle to scale under such conditions. This paper introduces a conceptual framework that combines two digital twins to accelerate and systematize the scientific discovery process. The first, a Phenomenon Digital Twin, simulates the physical system under investigation. The second, a Scientific Discovery Digital Twin, uses Generative Flow Networks (GFlowNets) to intelligently explore the experimental design space and prioritize informative experiments. This dual-DT architecture enables iterative refinement of models while optimizing data collection under budget constraints. The framework is demonstrated through an illustrative case study on plasma-enhanced deposition, a materials science domain characterized by poorly understood phenomena and large configuration spaces. While the proposed approach is still in its conceptual stage, it outlines a pathway toward adaptive, AI-assisted scientific exploration applicable across disciplines.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".