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https://github.com/noegroup/dlqmc-project


https://github.com/noegroup/dlqmc-project

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README

        

This repository contains computational resources for the manuscript *Deep neural network solution of the electronic Schrödinger equation*.

## Prerequisites

- Python requirements are managed with [Poetry](https://poetry.eustace.io) and specified in the `pyproject.toml` and `poetry.lock` files.
- Data files in `data/raw/` available at DOI:[10.6084/m9.figshare.12720569](https://doi.org/10.6084/m9.figshare.12720569).

## Installing

Install using Poetry.

```
git clone https://github.com/noegroup/dlqmc-project
cd dlqmc-project
poetry install
```

## Usage

$ dlqmc prepare --help
Usage: dlqmc prepare [OPTIONS] PATH COMMAND [ARGS]...

Options:
--help Show this message and exit.

Commands:
all-systems
custom
cyclobutadiene
hyperparam-scan-co2
sampling
script

## File organization

- `src/dlqmc/`: Python package `dlqmc` used in scripts and notebooks.
- `scripts/`: Python scripts for processing raw data in `data/raw/` to `data/final`.
- `data/extern/`: Reference data extracted from external sources.
- `data/final/`: Processed data in csv format used to generate manuscript figures.
- `notebooks/dl-qmc-figures.ipynb`: Jupyter notebook for generating manuscript figures.
- `extern/deepqmc/`: Git submodule with the [DeepQMC](https://github.com/deepqmc/deepqmc) package.
- `assets/`: Figure fragments generated by external tools.
- `Makefile`: Helper for managing calculations on a cluster.
- `pub/`: Git submodule with the manuscript repository (not public).