Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
awesome-textmining-materials-science
Collection of papers on text mining for materials science
https://github.com/hhaoyan/awesome-textmining-materials-science
- spaCy - built deep learning models for tokenization, NER, POS, dependency parsing, word2vec, etc.
- textacy - /post- processing of text used in conjunction with spaCy, such as text normalization, garbage text cleaning, extraction of ngrams, entities, etc.
- ChemDataExtractor - fledged toolkit for sentence segmentation, tokenization, chemical NER, and extracting chemical information.
- PDFMiner
- textract - to-plain-text converters including PDF files.
- tesseract - source C++ OCR tool based on LSTM that supports many languages.
- Google Cloud OCR
- ImageDataExtractor: A Tool To Extract and Quantify Data from Microscopy Images by Mukaddem et al
- Machine-learned and codified synthesis parameters of oxide materials by Kim et al
- Text-mined dataset of inorganic materials synthesis recipes by Kononova et al
- Annotating and Extracting Synthesis Process of All-Solid-State Batteries from Scientific Literature by Kuniyoshi et al - solid-state battery articles.
- Auto-generated materials database of Curie and Néel temperatures via semi-supervised relationship extraction by Court et al
- An open experimental database for exploring inorganic materials by Zakutayev et al
- The Materials Science Procedural Text Corpus: Annotating Materials Synthesis Procedures with Shallow Semantic Structures by Mysore et al
- Pipelines for Procedural Information Extraction from Scientific Literature: Towards Recipes using Machine Learning and Data Science by Yang et al
- Named Entity Recognition and Normalization Applied to Large-Scale Information Extraction from the Materials Science Literature by Weston et al
- Automated Extraction of Chemical Synthesis Actions from Experimental Procedures by Vaucher et al - based/ML(Transformer) model to extract synthesis actions from experimental procedures.
- Automatically Extracting Action Graphs from Materials Science Synthesis Procedures by Mysore et al - based heuristics.
- Using Natural Language Processing Techniques to Extract Information on the Properties and Functionalities of Energetic Materials from Large Text Corpora by Elton et al
- Semi-supervised machine-learning classification of materials synthesis procedures by Huo et al
- Materials Synthesis Insights from Scientific Literature via Text Extraction and Machine Learning by Kim et al
- Virtual screening of inorganic materials synthesis parameters with deep learning by Kim et al
- Inorganic Materials Synthesis Planning with Literature-Trained Neural Networks by Kim et al
- Unsupervised word embeddings capture latent knowledge from materials science literature by Tshitoyan et al
- A Relation Aware Search Engine for Materials Science by Shah et al
- A Bayesian framework for materials knowledge systems by Kalidindi