Upload weights for screening-properties (credited re-host)

LICENSE

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+MIT License
+
+Copyright (c) [2025] [Marco Hoffmann, Thomas Specht, Fabian Jirasek]
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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+---
+license: mit
+library_name: pytorch
+tags: [chemistry, thermodynamics, activity-coefficients, excess-gibbs-energy, screening-properties]
+---
+
+# HANNA: binary activity coefficients
+
+HANNA is a hard-constraint neural network that predicts the excess Gibbs energy, and from it thermodynamically consistent activity coefficients, of liquid mixtures from SMILES, temperature and composition. This repository contains the **unchanged** binary ensemble (ten members), the ChemBERTa encoder and the input scalers that HANNA uses, so that screening-properties (the property package of the ScreeningTool process-screening framework) can download them on first use.
+
+**All credit goes to the original authors.**
+
+- Code and weights: [marco-hoffmann/HANNA](https://github.com/marco-hoffmann/HANNA), based on the prototype by [tspecht93/HANNA](https://github.com/tspecht93/HANNA) (MIT licence, see `LICENSE`).
+- Publications: Specht et al., *Chemical Science* 2024, [10.1039/D4SC05115G](https://pubs.rsc.org/en/Content/ArticleLanding/2024/SC/D4SC05115G), and the follow-up in *Nature Communications* (2026), [s41467-026-71430-y](https://www.nature.com/articles/s41467-026-71430-y).
+- The `models/ChemBERTa` encoder is the copy distributed with HANNA, derived from [DeepChem/ChemBERTa-77M-MTR](https://huggingface.co/DeepChem/ChemBERTa-77M-MTR). Please also credit the ChemBERTa authors.
+
+## Files
+
+| File | Content |
+| --- | --- |
+| `models/HANNA/ensemble/HANNA_parameters_binary{0-9}.pt` | ensemble state dicts (load with `weights_only=True`) |
+| `models/ChemBERTa/` | SMILES encoder and tokenizer (transformers format) |
+| `utils/scalers/*.pkl` | scikit-learn scalers for temperature and embeddings |
+
+The scalers are pickles. screening-properties checks their SHA-256 at a pinned revision before loading them.
+
+In screening-properties, HANNA predictions are fitted to NRTL parameters and accepted only up to a user-chosen maximum held-out ln γ error (`settings.max_error`).

models/ChemBERTa/added_tokens.json

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