Material intelligence by the convergence of artificial intelligence and robotic platforms
Bibliographic record
Abstract
The emerging interdisciplinary research of material intelligence through the convergence of artificial intelligence, robotic platforms, and material informatics has revolutionized the field of chemistry and material science. This shift enables precision and intelligence in materials research to avoid the problems of trial-and-error synthesis and labor-intensive characterization. The aim of this review is to present a comprehensive methodology that unifies three interlinked domains: data-guided rational design ("reading"), automation-enabled controllable synthesis ("doing"), and autonomy-facilitated inverse design ("thinking"). We critically examine how the integration of materials common discipline (i.e., rational design, controllable synthesis, inverse design) with interdisciplinary research (i.e., data, automation, autonomy), with an emphasis on cutting-edge research of artificial intelligence and robotics, collectively shape a closed-loop next paradigm of material intelligence, revolutionizing experimental, theoretical, software-driven and data-driven paradigms. Ultimately, this paper discusses how these insights drive the new paradigm of materials research, which seamlessly combines database, robotics, artificial intelligence, and even embodied intelligence to empower the full potential of material intelligence.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.013 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".