A Self-Consistent Field Theory Formalism for Sequence-Defined Polymers
Bibliographic record
Abstract
Sequence-defined polymers open up a high-dimensional space for molecular self-assembly and material design, requiring more advanced tools for exploration of this space. Here, classical self-consistent field theory (SCFT) is extended to sequence-defined polymers; this new formalism uses a set of four-dimensional fields and a set of descriptor functions representing the polymer sequence. The self-consistent equations resulting from this formalism are invariant to sequence complexity. Its computational cost is shown to be a factor N s (number of points used to discretize the polymer contour length) more expensive than the current SCFT formalism. A variety of copolymers of varying complexity are simulated with the new sequence SCFT formalism and are shown to match literature results. Additionally, sequence SCFT is shown to enable a method of investigating the configurational distribution of polymer chains which make up a phase-separated structure. The ease at which this new formalism handles complex polymer sequences will enable new discoveries of design rules governing sequence-controlled polymer properties.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".