Bibliographic record
Abstract
In 2011, U.S. President Barack Obama proposed the Material Genome Project. Want to high high-speed and low-cost methods to develop material science, which promoted the rapid development of materials informatics. Materials informatics is an interdisciplinary field that employs the principles of informatics to enhance the comprehension, utilization, and advancement of materials within the realm of materials science. Materials informatics can take multiple approaches and influence many aspects of new material development. Material informatics will cause great changes in the material industry and promote the rapid development of materials. A variety of materials informatics schemes have been developed to analyze materials, and there have been many successful cases worth applying. This article introduces the basic concepts and main research fields of materials informatics and describes four main steps in materials informatics: data collection, data processing, the establishment of a database, construction of a model. The main applications of materials informatics include simulation and prediction of material properties, development of new materials, material optimization and performance improvement. Thinking about the difficult problems in the field of materials informatics for application.
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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.010 | 0.022 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 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".