APPLICATION PROGRESS OF MATERIALS GENOME TECHNOLOGY IN THE FIELD OF NEW ENERGY MATERIALS
Bibliographic record
Abstract
Materials genome integrates high-throughput computing, high-throughput preparation, high-throughput detection and database systems of materials. It is a "paradigm revolution" in materials research and development. With its profound scientific connotation and significant application potential, it will accelerate New materials discovery and applications. This article focuses on the use of materials genome in the research and development of new energy materials to shorten the "discovery-development-production-application" cycle of new energy materials. It introduces the internationally representative Materials Project and OQMD two material genome platforms, as well as some important the application of materials genome computing technologies, such as material conformation characterization, high-throughput computing and screening, machine learning, neural network technology, optimization algorithms and new high-throughput preparation and characterization technologies, in the research and development of new energy materials, and the next step the development of materials genome puts forward prospects, such as developing high-precision high-throughput computing, using artificial intelligence to develop high-throughput experimental systems and platforms, generating material big data, and making full use of material big data through intelligent computing to create computing and experiments. The integrated materials genome big data artificial intelligence system accelerates the discovery and application of new energy materials.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".