ReviewJACS Au2024
From Data to Discovery: Recent Trends of Machine Learning in Metal-Organic Frameworks.
Review in JACS Au, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 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
15 citing papers in PubMed.
- Multimodal Transformer for Sample-Aware Prediction of Metal-Organic Framework Properties.ACS central science · 2026Article
- Carboxylate-Ligand-Based Coordination Polymers and Metal Complexes: From Structural Diversity and Supramolecular Descriptors to Function-Oriented Design.Molecules (Basel, Switzerland) · 2026Review
- GFN1-xTB-Assisted Machine Learning for Electronic Structure Screening of Metal-Organic Frameworks.Journal of chemical theory and computation · 2026Article
- The data-only illusion in materials discovery.Nature materials · 2026Article
- Digital Reticular Chemistry: How Artificial Intelligence is Redefining Covalent Organic Framework Research.JACS Au · 2026Review
- Crystalline Framework Electrodes for Hybrid Supercapacitors: Device-Oriented Design From Metal-Organic and Covalent Organic Frameworks to Practical Hybrids.Chemical record (New York, N.Y.) · 2026Review
- Data Management and Analysis of Metal-Organic Framework Synthesis Using Data Models.Journal of chemical information and modeling · 2026Article
- The Application of Metal-Organic Frameworks as Drug Delivery Systems: From the Perspective of Molecular Dynamics Simulations.MedComm · 2026Review
- Optimizing cross-domain transfer for universal machine learning interatomic potentials.Nature communications · 2026Article
- RetNeXt: A Pretrained Model for Transfer Learning Across the MOF Adsorption Space.Journal of chemical information and modeling · 2026Article
- Synthetic Aspects and Characterization Needs in MOF Chemistry - from Discovery to Applications.Advanced materials (Deerfield Beach, Fla.) · 2025Review
- MOFGPT: Generative Design of Metal-Organic Frameworks using Language Models.Journal of chemical information and modeling · 2025Article
- Artificial Intelligence Paradigms for Next-Generation Metal-Organic Framework Research.Journal of the American Chemical Society · 2025Review
- The Design of Intrinsically Conductive Metal-Organic Frameworks for Thermoelectric Materials.Small science · 2025Article
- Covalent integration of polymers and porous organic frameworks.Frontiers in chemistry · 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
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Renowned for their high porosity and structural diversity, metal-organic frameworks (MOFs) are a promising class of materials for a wide range of applications. In recent decades, with the development of large-scale databases, the MOF community has witnessed innovations brought by data-driven machine learning methods, which have enabled a deeper understanding of the chemical nature of MOFs and led to the development of novel structures. Notably, machine learning is continuously and rapidly advancing as new methodologies, architectures, and data representations are actively being investigated, and their implementation in materials discovery is vigorously pursued. Under these circumstances, it is important to closely monitor recent research trends and identify the technologies that are being introduced. In this Perspective, we focus on emerging trends of machine learning within the field of MOFs, the challenges they face, and the future directions of their development.
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.