mofsyncondition 0.1.1

A python too for extracting synthesis condition of MOFs directly from journal articles in any file format (html, xml and pdf

mofsyncondition

mofsyncondition is a Python module for automatically extracting synthesis conditions of metal–organic frameworks (MOFs) from scientific journal articles.

The module reads HTML files or PDF-derived text files, uses machine learning models to identify paragraphs describing synthetic protocols and then extracts relevant synthesis conditions. In its current state, the extraction of synthesis conditions is primarily performed using intelligent regular expressions. The resulting dataset is being used to fine-tune a large language model (LLM) for MOFs.

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Overview

Extracting synthesis conditions from MOF literature is a key challenge in data-driven materials discovery. mofsyncondition addresses this problem by:

• Reading journal articles in HTML, pdf or xml format
• Identifying synthesis-related paragraphs using ML-based classification
• Extracting structured synthesis conditions from unstructured text
• Generating datasets suitable for machine learning and LLM training

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Key Features

• Support for HTML and PDF-derived text inputs
• ML-based identification of synthesis protocols
• Regex-driven extraction of synthesis conditions
• Modular and extensible Python design
• Scalable for large literature datasets

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Extracted Synthesis Information

The module aims to extract synthesis parameters such as:

• Metal precursors
• Organic linkers
• Solvents
• Additives / modulators
• Reaction temperature
• Reaction time
• pH (when available)
• Synthetic methods (e.g. solvothermal, hydrothermal)
• Pressure and humidity (when available)
• Name of MOF or formular is provided

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Installation

Clone the repository and install the package locally:

git clone https://github.com/bafgreat/mofsyncondition.git
cd mofsyncondition
pip install .

PYPI

The module can be install using PYPI

   pip install mofsyncondition

Usage

1. Extract synthetic paragraph from file

Assuming you have different files and wish to extract list of paragraphs describing synthesis simply run the following code.

    from mofsyncondition.synthesis_conditions import extractor

    # filepaths
    pdf_file_path = '../filename.pdf'
    html_file_path = '../filename.html'
    xml_file_path = '../filename.xml'

    # declare extractor class
    text_extractor = extractor.MOFSynConditionExtractor()

    # PDF extraction

    list_of_paragraphs = text_extractor.read_file(pdf_file_path)
    synthetic_paragraphs = text_extractor.get_synthetic_paragraph(list_of_paragraphs)


    # html extraction

    list_of_paragraphs = text_extractor.read_file(html_file_path)
    synthetic_paragraphs = text_extractor.get_synthetic_paragraph(list_of_paragraphs)


    # xml extraction

    list_of_paragraphs = text_extractor.read_file(xml_file_path)
    synthetic_paragraphs = text_extractor.get_synthetic_paragraph(list_of_paragraphs)

By default the paragraph sentiment model uses NN_tfv. Below is a list of other models.

ML Model Performance (5-Fold Cross-Validation Averages)

Rank	Model	Avg Accuracy	Avg Precision	Notes
1	SVM_tfv	0.9905	0.8163	Best overall accuracy
2	NN_tfv	0.9903	0.8143	Default model
3	RF_tfv	0.9904	0.7730	High accuracy, lower precision
4	RF_CV	0.9902	0.7692	Stable but conservative
5	NN_CV	0.9889	0.8240	High precision
6	LR_tfv	0.9895	0.7853	Fast baseline
7	LR_CV	0.9885	0.8040	Balanced baseline
8	SVM_CV	0.9885	0.8124	Robust alternative
9	DT_CV	0.9865	0.7795	Interpretable
10	DT_tfv	0.9851	0.7692	Simple model
11	NB_CV	0.9837	0.8337	Highest precision
12	NB_tfv	0.9657	0.0232	Not recommended

To use any model, simply add the name of the model to the function. e.g

   list_of_paragraphs = text_extractor.read_file(xml_file_path)
   synthetic_paragraphs = text_extractor.get_synthetic_paragraph(list_of_paragraphs, model="NN_CV")

2. Extract paragaraph level synthetic condition from file

Suppose you have an document (pdf, html, xml) and wish to extract all synthesis conditions. The below lines of code it the faster way to do so. This is faster than using transformer models and take large documents and parse thousand of files.

import spacy
from mofsyncondition.synthesis_conditions.extractor import MOFSynConditionExtractor
from mofsyncondition.io import filetyper

data_extractor = MOFSynConditionExtractor()

transformer_dataset = []
standard_dataset = []
file_path = "./data_test/Test2.pdf"

all_files = ["./data_test/Test2.pdf", "./data_test/ABAFUH.xml", "./data_test/Test3.html"]
for file_path in all_files:
    syn_data  = data_extractor.syn_data_from_document(file_path)
    for paragraph, data_style_1, data_style_2 in syn_data:
        transformer_dataset.append({'paragraph':paragraph, "condition":data_style_1})
        standard_dataset.append({'paragraph':paragraph, "condition":data_style_2})

LICENSE

MIT license