# A general-purpose material property data extraction pipeline from large polymer corpora using Natural Language Processing

arXiv: https://arxiv.org/abs/2209.13136 · HTML: https://mlsanity.com/paper/2209.13136/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 89 references that are in the map.
- First posted on arXiv 27 Sep 2022.

## Builds on (most cited first, 22 of 22)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation](https://mlsanity.com/paper/1609.08144/) (2016)
- [Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books](https://mlsanity.com/paper/1506.06724/) (2015)
- [Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing](https://mlsanity.com/paper/2007.15779/) (2020)
- [Bidirectional LSTM-CRF Models for Sequence Tagging](https://mlsanity.com/paper/1508.01991/) (2015)
- [ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission](https://mlsanity.com/paper/1904.05342/) (2019)
- [FinBERT: Financial Sentiment Analysis with Pre-trained Language Models](https://mlsanity.com/paper/1908.10063/) (2019)
- [Snorkel: Rapid Training Data Creation with Weak Supervision](https://mlsanity.com/paper/1711.10160/) (2017)
- [BOND: BERT-Assisted Open-Domain Named Entity Recognition with Distant Supervision](https://mlsanity.com/paper/2006.15509/) (2020)
- [Fast WordPiece Tokenization](https://mlsanity.com/paper/2012.15524/) (2020)
- [MatSciBERT: A Materials Domain Language Model for Text Mining and Information Extraction](https://mlsanity.com/paper/2109.15290/) (2021)
- [The Materials Science Procedural Text Corpus: Annotating Materials Synthesis Procedures with Shallow Semantic Structures](https://mlsanity.com/paper/1905.06939/) (2019)
- [Polymer Informatics: Current Status and Critical Next Steps](https://mlsanity.com/paper/2011.00508/) (2020)
- [NERO: A Neural Rule Grounding Framework for Label-Efficient Relation Extraction](https://mlsanity.com/paper/1909.02177/) (2019)
- [BERTifying the Hidden Markov Model for Multi-Source Weakly Supervised Named Entity Recognition](https://mlsanity.com/paper/2105.12848/) (2021)
- [PRBoost: Prompt-Based Rule Discovery and Boosting for Interactive Weakly-Supervised Learning](https://mlsanity.com/paper/2203.09735/) (2022)
- [WNUT-2020 Task 1 Overview: Extracting Entities and Relations from Wet Lab Protocols](https://mlsanity.com/paper/2010.14576/) (2020)
- [Loss of New Ideas: Potentially Long-lasting Effects of the Pandemic on Scientists](https://mlsanity.com/paper/2107.13073/) (2021)

## Built on by (most cited first, 22 of 22)

- [An Interdisciplinary Outlook on Large Language Models for Scientific Research](https://mlsanity.com/paper/2311.04929/) (2023)
- [MaScQA: A Question Answering Dataset for Investigating Materials Science Knowledge of Large Language Models](https://mlsanity.com/paper/2308.09115/) (2023)
- [A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools](https://mlsanity.com/paper/2506.20743/) (2025)
- [TritonDFT: Automating DFT with a Multi-Agent Framework](https://mlsanity.com/paper/2603.03372/) (2026)
- [Automated Extraction of Material Properties using LLM-based AI Agents](https://mlsanity.com/paper/2510.01235/) (2025)
- [Accelerating materials discovery for polymer solar cells: Data-driven insights enabled by natural language processing](https://mlsanity.com/paper/2402.19462/) (2024)
- [Solar-GECO: Perovskite Solar Cell Property Prediction with Geometric-Aware Co-Attention](https://mlsanity.com/paper/2511.19263/) (2025)
- [Accelerated materials language processing enabled by GPT](https://mlsanity.com/paper/2308.09354/) (2023)
- [NLP for Knowledge Discovery and Information Extraction from Energetics Corpora](https://mlsanity.com/paper/2402.06964/) (2024)
- [MaterioMiner -- An ontology-based text mining dataset for extraction of process-structure-property entities](https://mlsanity.com/paper/2408.04661/) (2024)
- [Sampling Latent Material-Property Information From LLM-Derived Embedding Representations](https://mlsanity.com/paper/2409.11971/) (2024)
- [Semantic Embeddings of Chemical Elements for Enhanced Materials Inference and Discovery](https://mlsanity.com/paper/2502.14912/) (2025)
- [Enhanced Multi-Tuple Extraction for Alloys: Integrating Pointer Networks and Augmented Attention](https://mlsanity.com/paper/2503.06861/) (2025)
- [Information fusion strategy integrating pre-trained language model and contrastive learning for materials knowledge mining](https://mlsanity.com/paper/2506.12516/) (2025)
- [BioPars: A Pretrained Biomedical Large Language Model for Persian Biomedical Text Mining](https://mlsanity.com/paper/2506.21567/) (2025)
- [Language-Native Materials Processing Design by Lightly Structured Text Database and Reasoning Large Language Model](https://mlsanity.com/paper/2509.06093/) (2025)
- [Reliable End-to-End Material Information Extraction from the Literature with Source-Tracked Multi-Stage Large Language Models](https://mlsanity.com/paper/2510.05142/) (2025)
- [Retrieval Augmented Generation of Literature-derived Polymer Knowledge: The Example of a Biodegradable Polymer Expert System](https://mlsanity.com/paper/2602.16650/) (2026)
- [Large language model-enabled automated data extraction for concrete materials informatics](https://mlsanity.com/paper/2604.22938/) (2026)
- [Text-guided flow matching enables sample-efficient crystal structure generation](https://mlsanity.com/paper/2609.01076/) (2026)
- [Domain-Adaptive Pretraining Enhances Water Treatment Semantic Representation for Large-Scale Structured Literature Mining](https://mlsanity.com/paper/2609.26034/) (2026)
- [Adapt Semantics, Not Structure: Few-Instance Schema Calibration for Scientific PDF Extraction](https://mlsanity.com/paper/2609.34841/) (2026)

## Abstract (arXiv)

The ever-increasing number of materials science articles makes it hard to infer chemistry-structure-property relations from published literature. We used natural language processing (NLP) methods to automatically extract material property data from the abstracts of polymer literature. As a component of our pipeline, we trained MaterialsBERT, a language model, using 2.4 million materials science abstracts, which outperforms other baseline models in three out of five named entity recognition datasets when used as the encoder for text. Using this pipeline, we obtained ~300,000 material property records from ~130,000 abstracts in 60 hours. The extracted data was analyzed for a diverse range of applications such as fuel cells, supercapacitors, and polymer solar cells to recover non-trivial insights. The data extracted through our pipeline is made available through a web platform at https://polymerscholar.org which can be used to locate material property data recorded in abstracts conveniently. This work demonstrates the feasibility of an automatic pipeline that starts from published literature and ends with a complete set of extracted material property information.