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[![ZnTrack](https://img.shields.io/badge/Powered%20by-ZnTrack-%23007CB0)](https://zntrack.readthedocs.io/en/latest/)
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[![zincware](https://img.shields.io/badge/Powered%20by-zincware-darkcyan)](https://github.com/zincware)
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# Structure Database Repository
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# Collaboration on Machine Learned Potentials with IPSuite: A Modular Framework for Learning-on-the-Fly
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This repository contains the workflows for machine learned potentials for DMSO.
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This repository contains simulation data that can be loaded via [ZnTrack](https://zntrack.readthedocs.io).
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In addition to [ZnTrack](https://zntrack.readthedocs.io) an [IPSuite](https://github.com/zincware/IPSuite) installation and [DVC-S3](https://dvc.org/doc/command-reference/remote/add) support are also required.
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To use this repository make sure, you've installed the correct versions of [IPSuite](https://github.com/zincware/IPSuite), [apax](https://github.com/apax-hub/apax), [MACE](https://github.com/ACEsuit/mace) and [NequIP](https://github.com/mir-group/nequip).
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Furthermore, to access the data you need to `pip install dvc-s3`.
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To get started, clone this repository:
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```bash
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pip install ipsuite zntrack dvc[s3]
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git clone https://github.com/IPSProjects/DMSO
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cd DMSO
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```
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The following example provides a list of [ase.Atoms](https://wiki.fysik.dtu.dk/ase/ase/atoms.html) that were generated during geometry optimization of water using [CP2K](https://www.cp2k.org/).
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For more information have a look at the branch: https://github.com/IPSProjects/SDR/tree/water
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## apax
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```bash
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git checkout apax
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dvc pull -j 1
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```
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## MACE
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```bash
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git checkout mace
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dvc pull -j 1
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```
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```python
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import zntrack
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import ase
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## MACE (small)
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```bash
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git checkout mace_small
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dvc pull -j 1
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```
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node = zntrack.from_rev("H2O-x10", remote="https://github.com/IPSProjects/SDR", rev="water")
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node.atoms: list[ase.Atoms]
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## NequIP
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```bash
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git checkout nequip
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dvc pull -j 1
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```

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