ArticleNature communications2024
ChatMOF: an artificial intelligence system for predicting and generating metal-organic frameworks using large language models.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 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
47 citing papers in PubMed.
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- Machine Learning-Driven Nanoscale Synthesis for Electrocatalytic Performance: From Data-Driven Methodologies to Closed-Loop Optimization.Advanced materials (Deerfield Beach, Fla.) · 2026Review
- Carboxylate-Ligand-Based Coordination Polymers and Metal Complexes: From Structural Diversity and Supramolecular Descriptors to Function-Oriented Design.Molecules (Basel, Switzerland) · 2026Review
- Biomacromolecule-MOF Composites for Intracellular Delivery: From Empirical Construction to Rational Design and AI-Assisted Screening.Small (Weinheim an der Bergstrasse, Germany) · 2026Review
- Interrogating the synthetic likelihood of metal-organic frameworks: a digital discovery perspective.Chemical science · 2026Review
- Machine Learning-Empowered Electromagnetic Wave Absorbing Materials: From Forward Prediction to Generative Inverse Design.Molecules (Basel, Switzerland) · 2026Review
- TeLLAgent: a dual-agent framework for reliable scientific discovery with tool-enhanced LLMs.Chemical science · 2026Article
- Inverse Design of Metal-Organic Polyhedra through Molecular Fragmentation and Evolutionary Optimisation.Journal of chemical information and modeling · 2026Article
- A conversational multi-agent AI system for automated plant phenotyping.Nature communications · 2026Article
- Verification and execution of the scientific literature via chemputation augmented by large language models.Communications chemistry · 2026Article
- MDCrow: automating molecular dynamics workflows with large language models.Machine learning: science and technology · 2026Article
- A survey on large language models in biology and chemistry.Experimental & molecular medicine · 2026Review
- Artificial Intelligence at the Intersection of Chemistry and Materials Science.AI (Basel, Switzerland) · 2026Article
- General-Purpose Models for the Chemical Sciences: LLMs and Beyond.Chemical reviews · 2026Review
- Artificial intelligence-driven approaches for materials design and discovery.Nature materials · 2026Review
- AI-driven quantitative review of mobility-stability trade-off in oxide semiconductors.Nano convergence · 2026Review
- iDesignGPT enhances conceptual design via large language model agentic workflows.Nature communications · 2026Article
- A multimodal dataset of causal mechanisms in materials science literature.Scientific data · 2026Article
- Porosity in electrochemical energy storage materials for superior performance gaining: insight into property performance and looking ahead.Frontiers in chemistry · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
ChatMOF is an artificial intelligence (AI) system that is built to predict and generate metal-organic frameworks (MOFs). By leveraging a large-scale language model (GPT-4, GPT-3.5-turbo, and GPT-3.5-turbo-16k), ChatMOF extracts key details from textual inputs and delivers appropriate responses, thus eliminating the necessity for rigid and formal structured queries. The system is comprised of three core components (i.e., an agent, a toolkit, and an evaluator) and it forms a robust pipeline that manages a variety of tasks, including data retrieval, property prediction, and structure generations. ChatMOF shows high accuracy rates of 96.9% for searching, 95.7% for predicting, and 87.5% for generating tasks with GPT-4. Additionally, it successfully creates materials with user-desired properties from natural language. The study further explores the merits and constraints of utilizing large language models (LLMs) in combination with database and machine learning in material sciences and showcases its transformative potential for future advancements.
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.