ReviewChemical reviews2020
Big-Data Science in Porous Materials: Materials Genomics and Machine Learning.
Review in Chemical reviews, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 98 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
98 citing papers in PubMed.
- Predicting antibacterial activity of silver nanoparticles using physicochemical descriptors.iScience · 2026Article
- Envisioning AI-empowered ecological, environmental, and health protection: Welcome submissions of interdisciplinary research.Eco-Environment & Health · 2026Article
- The Power of Tiny Spaces.JACS Au · 2026Review
- Interaction topology theory deciphers multiscale codes of MOF-like materials.Science advances · 2026Article
- Manipulating Thermal Transport of 2D MOFs by Hierarchical Structural Design.Small (Weinheim an der Bergstrasse, Germany) · 2026Article
- Digital Reticular Chemistry: How Artificial Intelligence is Redefining Covalent Organic Framework Research.JACS Au · 2026Review
- Recent Synthesis Routes to Previously Inaccessible Zeolite Porosity: From Ultrasmall Pore to Ultraporous Materials.ACS applied materials & interfaces · 2026Review
- Predicting Interfacial Pull-Out Performance of Nano-BPolymers · 2026Article
- Review
- Data-Driven Engineering of Antimicrobial Nanomaterials for Food Safety and Biomedical Systems.Nanomaterials (Basel, Switzerland) · 2026Review
- Predicting the Thermodynamic Limits of Metal-Organic Framework Metastability.Journal of the American Chemical Society · 2026Article
- Decoding the unseen: unsupervised anomaly detection in metal-organic frameworks for discovery beyond the norm.Chemical science · 2026Article
- Machine learning guided discovery of water stable metal-organic frameworks for photocatalytic hydrogen production.Chemical science · 2026Article
- Oxidative Ring-Opening of Dimethylfuran in Zeolitic Imidazolate Frameworks through Computational Design.The journal of physical chemistry. C, Nanomaterials and interfaces · 2026Article
- 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
- Machine learning in next-generation AEM fuel cells: a systematic review.RSC advances · 2026Review
- FibreCastML: an open web platform for predicting electrospun nanofibre diameter distributions for biomedical applications.Frontiers in bioengineering and biotechnology · 2026Article
- Computational Modeling of Reticular Materials: The Past, the Present, and the Future.Advanced materials (Deerfield Beach, Fla.) · 2025Review
- Synthetic Aspects and Characterization Needs in MOF Chemistry - from Discovery to Applications.Advanced materials (Deerfield Beach, Fla.) · 2025Review
38 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
By combining metal nodes with organic linkers we can potentially synthesize millions of possible metal-organic frameworks (MOFs). The fact that we have so many materials opens many exciting avenues but also create new challenges. We simply have too many materials to be processed using conventional, brute force, methods. In this review, we show that having so many materials allows us to use big-data methods as a powerful technique to study these materials and to discover complex correlations. The first part of the review gives an introduction to the principles of big-data science. We show how to select appropriate training sets, survey approaches that are used to represent these materials in feature space, and review different learning architectures, as well as evaluation and interpretation strategies. In the second part, we review how the different approaches of machine learning have been applied to porous materials. In particular, we discuss applications in the field of gas storage and separation, the stability of these materials, their electronic properties, and their synthesis. Given the increasing interest of the scientific community in machine learning, we expect this list to rapidly expand in the coming years.
Indexed as
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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.