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README.md

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[![Requires Python 3.10+](https://img.shields.io/badge/Python-3.10+-blue.svg?logo=python&logoColor=white)](https://python.org/downloads)
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NanoParticleTools tools is a python module that facilitates monte carlo simulation of Upconverting Nanoparticles (UCNP) using [RNMC](https://github.com/BlauGroup/RNMC).
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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).
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# Using NanoParticleTools
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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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Using only the machine learning capabilities within NanoParticleTools does not require the installation of [RNMC](https://github.com/BlauGroup/RNMC)
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## Installation
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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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```bash
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```bash
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pip install .
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```
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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.
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### Training and Using Machine Learning Models
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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).
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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.
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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)
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can be downloaded from Figshare.
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## Running Simulations
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An example of local execution can be seen below.
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```
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Refer to the maggma [Stores documentation](https://materialsproject.github.io/maggma/getting_started/stores/) for more information.
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### High-throughput simulations
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## Running the Builder
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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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