ChemNLP is an innovative software package designed to process chemical information extracted from scientific literature. Its advanced natural language processing capabilities make it a valuable tool for researchers and developers in the field of materials chemistry. In this article, we’ll delve into how to install and use ChemNLP effectively.

Introduction

As the realm of chemistry becomes intertwined with technology, understanding and utilizing software like ChemNLP is essential for researchers. Below, we outline the steps needed to get started with ChemNLP.

Installation

To seamlessly integrate ChemNLP into your workflow, follow these steps to install it within a conda environment:

• First, create a conda environment:

conda create --name chemnlp python=3.9

• Activate the environment:

source activate chemnlp

Now, choose your installation method:

Method 1: Using setup.py

• Clone the repository and navigate to it:

git clone https://github.com/usnistgov/chemnlp.git
cd chemnlp

• Install the package:

python setup.py develop
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Method 2: Using pip

• An alternative installation method via pip:

pip install chemnlp
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Examples

ChemNLP allows for various applications. Here, we will explain a few examples.

Parsing Chemical Formulas

• Run the following command:

run_chemnlp.py --file_path=chemnlp/tests/XYZ

Text Classification Example

• Use this command for classification:

python chemnlp/classification/scikit_class.py --csv_path chemnlp/sample_data/cond_mat_small.csv

For a comprehensive guide on installation and text classification, refer to the following resources:

• Google Colab example for installation and text classification
• Google Colab example for Text Generation with HuggingFace

Using the Web App

A user-friendly web-app for ChemNLP is available at:

• JARVIS-ChemNLP Web Application

References

• ChemNLP: A Natural Language Processing based Library for Materials Chemistry Text Data
• AtomGPT: Atomistic Generative Pretrained Transformer for Forward and Inverse Materials Design
• JARVIS-Leaderboard
• NIST-JARVIS Infrastructure

Troubleshooting

If you encounter issues during installation or usage, consider the following troubleshooting steps:

• Ensure your conda is properly installed and updated to the latest version.
• Check that the required Python version (3.9) is properly set in your conda environment.
• If installing via pip fails, try using the setup.py method instead.
• Consult the documentation provided in the GitHub repository for more detailed troubleshooting.

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At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.