UManSysProp v1.0: an online and open-source facility for molecular property prediction and atmospheric aerosol calculations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract. In this paper we describe the development and application of a new web-based facility, UManSysProp (http://umansysprop.seaes.manchester.ac.uk), for automating predictions of molecular and atmospheric aerosol properties. Current facilities include pure component vapour pressures, critical properties, and sub-cooled densities of organic molecules; activity coefficient predictions for mixed inorganic–organic liquid systems; hygroscopic growth factors and CCN (cloud condensation nuclei) activation potential of mixed inorganic–organic aerosol particles; and absorptive partitioning calculations with/without a treatment of non-ideality. The aim of this new facility is to provide a single point of reference for all properties relevant to atmospheric aerosol that have been checked for applicability to atmospheric compounds where possible. The group contribution approach allows users to upload molecular information in the form of SMILES (Simplified Molecular Input Line Entry System) strings and UManSysProp will automatically extract the relevant information for calculations. Built using open-source chemical informatics, and hosted at the University of Manchester, the facilities are provided via a browser and device-friendly web interface, or can be accessed using the user's own code via a JSON API (application program interface). We also provide the source code for all predictive techniques provided on the site, covered by the GNU GPL (General Public License) license to encourage development of a user community. We have released this via a Github repository (doi:10.5281/zenodo.45143). In this paper we demonstrate its use with specific examples that can be simulated using the web-browser interface.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it