The review traces the development of machine‑learning techniques for extracting materials data and knowledge from scientific literature, progressing from rule‑based pipelines to NLP models, transformer encoders, and finally large language models and agentic systems that handle text, tables, and figures. It highlights how these methods enable the creation of queryable materials databases, knowledge graphs, generic question answering, AI co‑scientists, chatbots, and closed‑loop discovery with autonomous labs. The paper critically evaluates datasets, extraction accuracy, computational costs, and offers practical guidelines, while outlining open challenges such as full tetrahedron extraction, benchmark scarcity, and terminology resolution.
arXiv:2604. 22938v2 Announce Type: replace-cross Abstract: The promise of data-driven materials discovery remains constrained by the scarcity of large, high-quality, and accessible experimental datasets.
Complementing the many efforts in providing semantic representations of concepts, notions, and entities in materials science, we report and illustrate a process by which we can automatically build a k...
arXiv:2609.17291v1 Announce Type: new Abstract: The quest for new materials increasingly relies on predictive models and comprehensive simulations that span scales from atomic to macroscopic levels....
arXiv:2604.13699v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have enabled agentic AI systems for scientific discovery, but most approaches remain limited to textbased reason...
The quest for new materials increasingly relies on predictive models and comprehensive simulations that span scales from atomic to macroscopic levels. However, essential data necessary for these model...
arXiv:2607. 09806v1 Announce Type: cross Abstract: Artificial intelligence (AI) is transforming scientific discovery, but its effectiveness is fundamentally limited by the availability of structured scientific knowledge.
The article examines how scientific literature, often used as a data source for AI in materials science, can contain hidden inaccuracies such as text-figure mismatches, ambiguous axis labels, unit inconsistencies, and missing measurement context. By tracing solid electrolyte conductivity values from original papers to curated datasets, the authors uncover recurrent errors that are numerically plausible yet hard to detect, leading to significant label noise in AI models. A cross-database example demonstrates that ambiguous reporting can cause a 100‑fold error in conductivity values, underscoring the need for traceable reporting, rigorous curation, and validation practices in AI-driven discovery.
arXiv:2606. 31366v1 Announce Type: cross Abstract: Driven by high-throughput experimentation, computational modeling, and artificial intelligence (AI), materials data has expanded at an unprecedented rate.
arXiv:2610.34391v1 Announce Type: cross Abstract: Scientific literature in materials physics is growing faster than researchers can manually inspect it, yet most retrieval tools rely on keyword match...
arXiv:2604.01452v2 Announce Type: replace Abstract: Scientific discovery is slowed by fragmented literature that requires excessive human effort to gather, analyze, and understand. AI tools, includin...
ElectroChemLLM is a multimodal large language model pipeline that extracts structured electrochemical records—such as electrolyte compositions, electrode materials, cell configurations, and measured physicochemical properties—from unstructured scientific PDFs. By combining hybrid document parsing with vision-capable LLM orchestration, it captures data across text, tables, and figures, achieving an F‑1 score of 0.97 on a benchmark of 232 battery‑electrolyte papers. The extracted data populate ElectroChemDB, a database of 15,000 research articles, which is then used to train machine learning models for electrolyte property prediction, demonstrating a full workflow from literature to materials discovery.
arXiv:2603. 03322v2 Announce Type: replace-cross Abstract: Recent advancements in Large Language Model (LLM) agents have demonstrated remarkable potential in automatic knowledge discovery.