Exploring the Possibilities and Limitations of a Nanomaterials Genome
Bibliographic record
Abstract
What are we going to do with the cornucopia of nanomaterials appearing in the open and patent literature, every day? Imagine the benefits of an intelligent and convenient means of categorizing, organizing, sifting, sorting, connecting, and utilizing this information in scientifically and technologically innovative ways by building a Nanomaterials Genome founded upon an all-purpose Periodic Table of Nanomaterials. In this Concept article, inspired by work on the Human Genome project, which began in 1989 together with motivation from the recent emergence of the Materials Genome project initiated in 2011 and the Nanoinformatics Roadmap 2020 instigated in 2010, we envision the development of a Nanomaterials Genome (NMG) database with the most advanced data-mining tools that leverage inference engines to help connect and interpret patterns of nanomaterials information. It will be equipped with state-of-the-art visualization techniques that rapidly organize and picture, categorize and interrelate the inherited behavior of complex nanomatter from the information programmed in its constituent nanomaterials building blocks. A Nanomaterials Genome Initiative (NMGI) of the type imagined herein has the potential to serve the global nanoscience community with an opportunity to speed up the development continuum of nanomaterials through the innovation process steps of discovery, structure determination and property optimization, functionality elucidation, system design and integration, certification and manufacturing to deployment in technologies that apply these versatile nanomaterials in environmentally responsible ways. The possibilities and limitations of this concept are critically evaluated in this article.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".