ReviewDigital discovery2024
Materials science in the era of large language models: a perspective.
Review in Digital discovery, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- Large language models accelerated health informatics screening framework predicts infrared triggered drug delivery performance of doped hydroxyapatite.Discover nano · 2026Article
- Dara: Automated Multiple-Hypothesis Phase Identification and Refinement from Powder X‑ray Diffraction.Chemistry of materials : a publication of the American Chemical Society · 2026Article
- Word embeddings as autonomous predictors in materials design-the effect of inherent variability on information transfer.Journal of cheminformatics · 2026Article
- SciAgents: Automating Scientific Discovery Through Bioinspired Multi-Agent Intelligent Graph Reasoning.Advanced materials (Deerfield Beach, Fla.) · 2025Article
- Explainable Synthesizability Prediction of Inorganic Crystal Polymorphs Using Large Language Models.Angewandte Chemie (International ed. in English) · 2025Article
- A Perspective on Foundation Models in Chemistry.JACS Au · 2025Review
- Data-Driven Search Algorithm for Discovery of Synthesizable Zeolitic Imidazolate Frameworks.JACS Au · 2025Article
- Open science falling behind in the era of artificial intelligence.Frontiers in research metrics and analytics · 2025Article
- Solutions for Lithium Battery Materials Data Issues in Machine Learning: Overview and Future Outlook.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Large Language Models (LLMs) have garnered considerable interest due to their impressive natural language capabilities, which in conjunction with various emergent properties make them versatile tools in workflows ranging from complex code generation to heuristic finding for combinatorial problems. In this paper we offer a perspective on their applicability to materials science research, arguing their ability to handle ambiguous requirements across a range of tasks and disciplines means they could be a powerful tool to aid researchers. We qualitatively examine basic LLM theory, connecting it to relevant properties and techniques in the literature before providing two case studies that demonstrate their use in task automation and knowledge extraction at-scale. At their current stage of development, we argue LLMs should be viewed less as oracles of novel insight, and more as tireless workers that can accelerate and unify exploration across domains. It is our hope that this paper can familiarise materials science researchers with the concepts needed to leverage these tools in their own research.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.