ArticleACS central science2023
ChatGPT Research Group for Optimizing the Crystallinity of MOFs and COFs.
Article in ACS central science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 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.
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Who cites it
34 citing papers in PubMed.
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- AI-Based Retrosynthesis of Known and Unexplored Covalent Organic Framework Monomers.Chemistry of materials : a publication of the American Chemical Society · 2026Article
- SmileyLlama: modifying large language models for directed chemical space exploration.Nature computational science · 2026Article
- Porous Organic Polymers: From Molecular Design to Scalable Technologies.Small (Weinheim an der Bergstrasse, Germany) · 2026Review
- Digital Reticular Chemistry: How Artificial Intelligence is Redefining Covalent Organic Framework Research.JACS Au · 2026Review
- Fine-tuning large language models to generate single-atom catalyst synthesis procedures.Communications chemistry · 2026Article
- Data Management and Analysis of Metal-Organic Framework Synthesis Using Data Models.Journal of chemical information and modeling · 2026Article
- Molecular and Computational Basis of Taste Perception: A Review toward the "Digital Language of Taste".ACS omega · 2026Review
- Foundation models for atomistic simulation of chemistry and materials.Nature reviews. Chemistry · 2026Review
- Molecular Modeling-Based Machine Learning for Accurate Prediction of Gas Diffusivity and Permeability in Metal-Organic Frameworks.ACS materials Au · 2026Article
- Recent advances of metal-organic frameworks (MOFs) for drug delivery, cancer imaging and theranostics.Theranostics · 2026Review
- Discovery of highly fluorescent covalent organic frameworks through AI-assisted iterative experiment-learning cycles.Nature chemistry · 2025Article
- From Carbon Nitrides to COFs: Opportunities and Prospects in Photocatalytic COAdvanced materials (Deerfield Beach, Fla.) · 2025Review
- Uncovering battery electrochemical mechanisms by artificial intelligence.National science review · 2025Review
- Artificial Intelligence as a Scientific Copilot in Analytical Chemistry: Transforming How We Write, Review, and Publish.Analytical chemistry · 2025Review
- Synergizing a knowledge graph and large language model for relay catalysis pathway recommendation.National science review · 2025Article
- Reasoning Language Model as Rule Finder: A Case Study on C-H Bond Activation Using 2D Metal-Organic Frameworks.ACS central science · 2025Article
- Artificial Intelligence Paradigms for Next-Generation Metal-Organic Framework Research.Journal of the American Chemical Society · 2025Review
- Pore engineering in metal-organic frameworks and covalent organic frameworks: strategies and applications.Chemical science · 2025Review
- Autonomous 'self-driving' laboratories: a review of technology and policy implications.Royal Society open science · 2025Review
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
We leveraged the power of ChatGPT and Bayesian optimization in the development of a multi-AI-driven system, backed by seven large language model-based assistants and equipped with machine learning algorithms, that seamlessly orchestrates a multitude of research aspects in a chemistry laboratory (termed the ChatGPT Research Group). Our approach accelerated the discovery of optimal microwave synthesis conditions, enhancing the crystallinity of MOF-321, MOF-322, and COF-323 and achieving the desired porosity and water capacity. In this system, human researchers gained assistance from these diverse AI collaborators, each with a unique role within the laboratory environment, spanning strategy planning, literature search, coding, robotic operation, labware design, safety inspection, and data analysis. Such a comprehensive approach enables a single researcher working in concert with AI to achieve productivity levels analogous to those of an entire traditional scientific team. Furthermore, by reducing human biases in screening experimental conditions and deftly balancing the exploration and exploitation of synthesis parameters, our Bayesian search approach precisely zeroed in on optimal synthesis conditions from a pool of 6 million within a significantly shortened time scale. This work serves as a compelling proof of concept for an AI-driven revolution in the chemistry laboratory, painting a future where AI becomes an efficient collaborator, liberating us from routine tasks to focus on pushing the boundaries of innovation.
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.