[WIP] Prototype trailblazing equilibration unit

[jthorton]

A PR to explore what a Trailblazing dynamic lambda optimiser might look like as a mixin which also does advanced equilibration in the process.

Method taken directly from Yank.

LLM / AI generated code disclosure

LLMs or other AI-powered tools (beyond simple IDE use cases) were used in this contribution: yes / no
If yes, please provide details here: No. Implemented by hand but reviewed with codex.

Checklist

• All new code is appropriately documented (user-facing code must have complete docstrings).
• Added a news entry, or the changes are not user-facing.
• Ran pre-commit: you can run pre-commit locally or comment on this PR with pre-commit.ci autofix.
• Filled in the AI generated code disclosure.

Manual Tests: these are slow so don't need to be run every commit, only before merging and when relevant changes are made (generally at reviewer-discretion).

• GPU integration tests
• example notebook testing
• packaging tests: run this for any large feature PRs or PRs that add test data.

Developers certificate of origin

• I certify that this contribution is covered by the MIT License here and the Developer Certificate of Origin at https://developercertificate.org/.

[jthorton]

pre-commit.ci autofix

[codecov]

Codecov Report

❌ Patch coverage is 0% with 140 lines in your changes missing coverage. Please review.
✅ Project coverage is 89.67%. Comparing base (6c88aae) to head (fe09da1).

Files with missing lines	Patch %	Lines
src/openfe/protocols/openmm_utils/trailblazing.py	0.00%	140 Missing ⚠️

Additional details and impacted files

@@            Coverage Diff             @@
##             main    #2176      +/-   ##
==========================================
- Coverage   95.04%   89.67%   -5.38%     
==========================================
  Files         206      207       +1     
  Lines       20514    20654     +140     
==========================================
- Hits        19498    18521     -977     
- Misses       1016     2133    +1117     

Flag	Coverage Δ
fast-tests	89.67% <0.00%> (?)
slow-tests	?

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