Assessment of a foundational machine-learned potential for energy ranking of molecular crystal polymorphs
Bibliographic record
Abstract
First-principles crystal structure prediction (CSP) of isolable polymorphs of organic compounds is a grand challenge in computational chemistry. The adoption of dispersion-correction density-functional theory (DFT) has allowed great strides to be made in the accuracy of the final energy ranking of candidate crystal structures. Consequently, CSP methods are seeing increasing use in development of new pharmaceuticals, organic electronics, energetic materials, and pigments, among other applications. However, lower-cost methods, such as classical force-field potentials, are still necessary for the early stages of CSP, where hundreds of thousands of candidates are commonly generated. Recently developed foundational machine-learned potentials represent a seductive alternative to force fields for this purpose due to their promise of near-DFT accuracy at a vastly reduced computational cost. In this work, the performance of the MACE-OFF23(M) machine-learned potential is assessed for geometry optimisation and energy ranking of candidate crystal structures of 28 compounds from the first seven CSP blind tests, as well as 12 helicene compounds. The performance of MACE-OFF23(M) is found to be highly dependent on the particular compound, providing good accuracy for compounds similar to those in its training set, but failing dramatically for compounds containing unusual functional groups (such as diazo) and organic salts. Physically motivated inclusion of long-range electrostatic interactions remains an open problem for development of foundational machine-learned potentials.
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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 | 0.004 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".