# Large Language Models as Master Key: Unlocking the Secrets of Materials Science with GPT

arXiv: https://arxiv.org/abs/2304.02213 · HTML: https://mlsanity.com/paper/2304.02213/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 36 references that are in the map.
- First posted on arXiv 5 Apr 2023.

## Builds on (most cited first, 8 of 8)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Bidirectional LSTM-CRF Models for Sequence Tagging](https://mlsanity.com/paper/1508.01991/) (2015)
- [MatSciBERT: A Materials Domain Language Model for Text Mining and Information Extraction](https://mlsanity.com/paper/2109.15290/) (2021)
- [SELFIES and the future of molecular string representations](https://mlsanity.com/paper/2204.00056/) (2022)
- [Structured information extraction from complex scientific text with fine-tuned large language models](https://mlsanity.com/paper/2212.05238/) (2022)
- [The Materials Science Procedural Text Corpus: Annotating Materials Synthesis Procedures with Shallow Semantic Structures](https://mlsanity.com/paper/1905.06939/) (2019)
- [Relation Extraction : A Survey](https://mlsanity.com/paper/1712.05191/) (2017)

## Built on by (most cited first, 30 of 30)

- [A Survey on Knowledge Distillation of Large Language Models](https://mlsanity.com/paper/2402.13116/) (2024)
- [A Comprehensive Survey of Scientific Large Language Models and Their Applications in Scientific Discovery](https://mlsanity.com/paper/2406.10833/) (2024)
- [HoneyComb: A Flexible LLM-Based Agent System for Materials Science](https://mlsanity.com/paper/2409.00135/) (2024)
- [DARWIN Series: Domain Specific Large Language Models for Natural Science](https://mlsanity.com/paper/2308.13565/) (2023)
- [LLaMP: Large Language Model Made Powerful for High-fidelity Materials Knowledge Retrieval and Distillation](https://mlsanity.com/paper/2401.17244/) (2024)
- [MicroVQA: A Multimodal Reasoning Benchmark for Microscopy-Based Scientific Research](https://mlsanity.com/paper/2503.13399/) (2025)
- [Sparks of Science: Hypothesis Generation Using Structured Paper Data](https://mlsanity.com/paper/2504.12976/) (2025)
- [SciQAG: A Framework for Auto-Generated Science Question Answering Dataset with Fine-grained Evaluation](https://mlsanity.com/paper/2405.09939/) (2024)
- [Large Language Model Agent as a Mechanical Designer](https://mlsanity.com/paper/2404.17525/) (2024)
- [SciSafeEval: A Comprehensive Benchmark for Safety Alignment of Large Language Models in Scientific Tasks](https://mlsanity.com/paper/2410.03769/) (2024)
- [Assessing Large Language Models in Mechanical Engineering Education: A Study on Mechanics-Focused Conceptual Understanding](https://mlsanity.com/paper/2401.12983/) (2024)
- [Integrating Chemistry Knowledge in Large Language Models via Prompt Engineering](https://mlsanity.com/paper/2404.14467/) (2024)
- [Geometry Informed Tokenization of Molecules for Language Model Generation](https://mlsanity.com/paper/2408.10120/) (2024)
- [A Survey on Symbolic Knowledge Distillation of Large Language Models](https://mlsanity.com/paper/2408.10210/) (2024)
- [MatViX: Multimodal Information Extraction from Visually Rich Articles](https://mlsanity.com/paper/2410.20494/) (2024)
- [Research on Personalized Financial Product Recommendation by Integrating Large Language Models and Graph Neural Networks](https://mlsanity.com/paper/2506.05873/) (2025)
- [Toward Reliable Ad-hoc Scientific Information Extraction: A Case Study on Two Materials Datasets](https://mlsanity.com/paper/2406.05348/) (2024)
- [Defining a New NLP Playground](https://mlsanity.com/paper/2310.20633/) (2023)
- [Research on Key Technologies for Cross-Cloud Federated Training of Large Language Models](https://mlsanity.com/paper/2410.19130/) (2024)
- [CataLM: Empowering Catalyst Design Through Large Language Models](https://mlsanity.com/paper/2405.17440/) (2024)
- [Knowledge Sharing in Manufacturing using Large Language Models: User Evaluation and Model Benchmarking](https://mlsanity.com/paper/2401.05200/) (2024)
- [Enhancing Exchange Rate Forecasting with Explainable Deep Learning Models](https://mlsanity.com/paper/2410.19241/) (2024)
- [HyperPIE: Hyperparameter Information Extraction from Scientific Publications](https://mlsanity.com/paper/2312.10638/) (2023)
- [Extracting Polymer Nanocomposite Samples from Full-Length Documents](https://mlsanity.com/paper/2403.00260/) (2024)
- [A Comprehensive Review of Large Language Models for Nanophotonics: From Surrogate Modeling to Autonomous Design](https://mlsanity.com/paper/2608.18279/) (2026)
- [MaTableGPT: GPT-based Table Data Extractor from Materials Science Literature](https://mlsanity.com/paper/2406.05431/) (2024)
- [MaterioMiner -- An ontology-based text mining dataset for extraction of process-structure-property entities](https://mlsanity.com/paper/2408.04661/) (2024)
- [Towards Efficient Large Language Models for Scientific Text: A Review](https://mlsanity.com/paper/2408.10729/) (2024)
- [RLDBF: Enhancing LLMs Via Reinforcement Learning With DataBase FeedBack](https://mlsanity.com/paper/2504.03713/) (2025)
- [Mechanisms of Matter: Language Inferential Benchmark on Physicochemical Hypothesis in Materials Synthesis](https://mlsanity.com/paper/2509.25281/) (2025)

## Abstract (arXiv)

The amount of data has growing significance in exploring cutting-edge materials and a number of datasets have been generated either by hand or automated approaches. However, the materials science field struggles to effectively utilize the abundance of data, especially in applied disciplines where materials are evaluated based on device performance rather than their properties. This article presents a new natural language processing (NLP) task called structured information inference (SII) to address the complexities of information extraction at the device level in materials science. We accomplished this task by tuning GPT-3 on an existing perovskite solar cell FAIR (Findable, Accessible, Interoperable, Reusable) dataset with 91.8% F1-score and extended the dataset with data published since its release. The produced data is formatted and normalized, enabling its direct utilization as input in subsequent data analysis. This feature empowers materials scientists to develop models by selecting high-quality review articles within their domain. Additionally, we designed experiments to predict the electrical performance of solar cells and design materials or devices with targeted parameters using large language models (LLMs). Our results demonstrate comparable performance to traditional machine learning methods without feature selection, highlighting the potential of LLMs to acquire scientific knowledge and design new materials akin to materials scientists.