Web application - Hierarchical generative and regressive machine learning for next generation materials screening

This repository contains the source code for web-application for the paper:

Ortega, Raul & Benediktsson, Bardi & Sechi, Renata & Jørgensen, Peter & Bhowmik, Arghya. (2023). "Materials Funnel 2.0 - Data-driven hierarchical search for exploration of vast chemical spaces". 10.26434/chemrxiv-2023-s8t6s-v2.

For the source code of the method implementation https://github.com/raulorteg/hts_funnel

About

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This is a web-application built to serve as an easier way to interact to the method proposed in "Materials Funnel 2.0 - Data-driven hierarchical search for exploration of vast chemical spaces" as a web-application.

• For the source code of the method implementation: https://github.com/raulorteg/hts_funnel

Installation

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1. Clone the repository: git clone https://github.com/raulorteg/hts_funnel-app
2. Create the python virtual environment (I use python 3.9.14): virtualenv --py=python3.9 hts_funnel_app
3. Activate virtualenv source hts_funnel_app/bin/activate
4. Install requirements python -m pip install -r requirements.txt Note: Your system might need a different torch installation (https://pytorch.org/get-started/locally/)

Requirements

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see the requirements.txt file

Usage

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From /app launch the web-application by running the command:

python -m uvicorn main:app --reload

Then open http://127.0.0.1:8000 on the browser to see the web-application.

Docker

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Build the Docker image

sudo docker build . --tag="funnelapp:latest"

Run the docker image

sudo docker run --rm -it -p 80:80/tcp funnelapp:latest

Then open http://0.0.0.0:80 on the browser to see the web-application.

Code formatting

Use isort 5.10.1 and black 22.10.0:

python -m pip install isort==5.10.1 black==22.10.0 

Sort the imports:

python -m isort <file_or_directory>

To format:

python -m black <file_or_directory>