The Design Space of E(3)-Equivariant Atom-Centered Interatomic Potentials
Bibliographic record
Abstract
Acknowledgements: This work was performed using resources provided by the Cambridge Service for Data Driven Discovery (CSD3), which is operated by the University of Cambridge Research Computing Service (www.csd3.cam.ac.uk) provided by Dell EMC and Intel using Tier-2 funding from the Engineering and Physical Sciences Research Council (capital grant number EP/T022159/1) and DiRAC funding from the Science and Technology Facilities Council (www.dirac.ac.uk). D.P.K. acknowledges support from AstraZeneca and the Engineering and Physical Sciences Research Council. C.O. is supported by Leverhulme Research Project grant number RPG-2017-191 and by the Natural Sciences and Engineering Research Council of Canada (NSERC) under funding reference number IDGR019381. Work at Harvard University was supported by Bosch Research, the US Department of Energy, Office of Basic Energy Sciences, under award number DE-SC0022199, the Integrated Mesoscale Architectures for Sustainable Catalysis (IMASC), an Energy Frontier Research Center, under award number DE-SC0012573 and by the NSF through Harvard University Materials Research Science and Engineering Center grant number DMR-2011754. A.M. is supported by US Department of Energy, Office of Science, Office of Advanced Scientific Computing Research, Computational Science Graduate Fellowship under award number DE-SC0021110. We acknowledge computing resources provided by the Harvard University FAS Division of Science Research Computing Group.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".