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13 | 13 | [](https://python.org/downloads)
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14 | 14 |
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15 | 15 |
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16 |
| -NanoParticleTools tools is a python module that facilitates monte carlo simulation of Upconverting Nanoparticles (UCNP) using [RNMC](https://github.com/BlauGroup/RNMC). |
| 16 | +NanoParticleTools tools is a python module that facilitates monte carlo simulation of Upconverting Nanoparticles (UCNP) using [RNMC](https://github.com/BlauGroup/RNMC) and analysis/prediction using deep learning, detailed in the [manuscript by Sivonxay et. al](https://chemrxiv.org/engage/chemrxiv/article-details/6769dc3a81d2151a02b75ef6). |
17 | 17 |
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18 | 18 | # Using NanoParticleTools
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19 | 19 | NanoParticleTools provides functionality to generate inputs for running Monte Carlo Simulations on nanoparticles and analyzing outputs. Monte Carlo simulation uses NMPC within the [RNMC](https://github.com/BlauGroup/RNMC) package. While NanoParticleTools provides wrapper functions to run the C++ based simulator, [RNMC](https://github.com/BlauGroup/RNMC) must be installed to perform simulations.
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20 | 20 |
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| 21 | +Using only the machine learning capabilities within NanoParticleTools does not require the installation of [RNMC](https://github.com/BlauGroup/RNMC) |
21 | 22 | ## Installation
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22 | 23 | To install NanoParticleTools to a python environment, clone the repository and use one of the following commands from within the NanoParticleTools directory
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23 | 24 | ```bash
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27 | 28 | ```bash
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28 | 29 | pip install .
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29 | 30 | ```
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| 31 | +Installation should take around 15 minutes on a normal desktop computer. NanoParticleTools can run on Python 3.10 and greater. The [setup.py](https://github.com/BlauGroup/NanoParticleTools/blob/main/setup.py) file includes pinned/constrained dependencies necessary for the installation. |
| 32 | + |
| 33 | +### Training and Using Machine Learning Models |
| 34 | + |
| 35 | +The functionality to train and use deep learning models to predict UCNP emission intensity using NanoParticleTools is embedded [here](https://github.com/BlauGroup/NanoParticleTools/tree/main/src/NanoParticleTools/machine_learning). |
| 36 | + |
| 37 | +Within the demos folder, we have included Jupyter notebooks with demos for (1) training a deep learning model on pre-compiled datasets of [RNMC](https://github.com/BlauGroup/RNMC) trajectories, (2) loading pre-trained models and predicting emission intensity for an arbitrary UCNP design, and (3) loading pre-trained models and predicting emission intensity on the pre-compiled UCNP datasets. |
| 38 | + |
| 39 | +Pre-compiled UCNP [datasets](https://figshare.com/s/49222bae78f228363897) (SUNSET) and pre-trained [models](https://figshare.com/articles/dataset/Hetero-GNN_Checkpoints/27941694/1?file=50919813) |
| 40 | + can be downloaded from Figshare. |
30 | 41 |
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31 | 42 | ## Running Simulations
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32 | 43 | An example of local execution can be seen below.
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@@ -77,9 +88,6 @@ data_store = MongoStore(<mongo credentials or URI here>)
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77 | 88 | ```
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78 | 89 | Refer to the maggma [Stores documentation](https://materialsproject.github.io/maggma/getting_started/stores/) for more information.
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79 | 90 |
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80 |
| -### High-throughput simulations |
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83 | 91 | ## Running the Builder
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84 | 92 | After running simulations, you may wish to average the outputs of trajectories obtained from the same recipe (using different dopant and simulation seeds). We have included a maggma builder in NanoParticleTools to easily group documents and perform the averaging. More information on builders can be found in the maggma [Builder documentation](https://materialsproject.github.io/maggma/reference/core_builder/)
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