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1 change: 1 addition & 0 deletions CHANGELOG.md
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### Fixed
- Don't print message about run being restored if the run is canceled or failed: this usually means that the module instance couldn't be started at all because the account tier doesn't support running that module instance. The other potential cause is if a module isn't available for a particular target and the user tries to use that combination.
- Print and store `trace` field from a run properly when it's either canceled or failed
- Add warning about nnxtb integration being experimental to docs
- Remove AI-generated mismatches page from docs

## 6.10.1
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2 changes: 2 additions & 0 deletions docs/nnxtb/overview.md
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# Overview | [Use NN-xTB](https://nnxtb.qdx.co)

> ⚠️ **Experimental Integration** — The NN-xTB integration with RushPy is currently experimental. You will likely encounter errors or unexpected behaviour.

NN-xTB (Neural Network extended Tight Binding) is a GPU-accelerated semi-empirical quantum chemistry method built by QDX. It reparameterizes the GFN2-xTB Hamiltonian with a neural network to approach DFT-level accuracy while retaining the interpretability and speed of tight-binding methods.

NN-xTB fills the gap between fast but approximate classical force fields and accurate but expensive *ab initio* quantum chemistry. It is designed for workflows where you need quantum-level electronic structure information — energies, forces, vibrational frequencies — but cannot afford the cost of full DFT or wavefunction methods across large numbers of structures.
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