ReviewACS central science2020
Too Many Materials and Too Many Applications: An Experimental Problem Waiting for a Computational Solution.
Review in ACS central science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 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
36 citing papers in PubMed, 143 citations in OpenAlex.
- Digital Reticular Chemistry: How Artificial Intelligence is Redefining Covalent Organic Framework Research.JACS Au · 2026Review
- Fast process-level screening of metal-organic frameworks for adsorption separation using 3D classical density functional theory.Molecular systems design & engineering · 2026Article
- Artificial Intelligence at the Intersection of Chemistry and Materials Science.AI (Basel, Switzerland) · 2026Article
- ReDD-COFFEE under the Lens: Revealing Adsorption and Separation Performances of Hypothetical COFs Using Molecular Simulations and Machine Learning.Industrial & engineering chemistry research · 2026Article
- Efficient Prediction of Multicomponent Adsorption Isotherms and Enthalpies of Adsorption in MOFs Using Classical Density Functional Theory.The journal of physical chemistry. B · 2026Article
- Molecular Modeling-Based Machine Learning for Accurate Prediction of Gas Diffusivity and Permeability in Metal-Organic Frameworks.ACS materials Au · 2026Article
- FFLAME: a fragment-to-framework learning approach for MOF potentials.Digital discovery · 2025Article
- Computational Modeling of Reticular Materials: The Past, the Present, and the Future.Advanced materials (Deerfield Beach, Fla.) · 2025Review
- Rational Design of Metal-Organic Frameworks for Pancreatic Cancer Therapy: from Machine Learning Screening to In Vivo Efficacy.Advanced materials (Deerfield Beach, Fla.) · 2025Article
- Retrospective Review on Reticular Materials: Facts and Figures Over the Last 30 Years.Advanced materials (Deerfield Beach, Fla.) · 2025Article
- Thermal transport mechanisms in ZIFs.Nature communications · 2025Article
- Connecting metal-organic framework synthesis to applications using multimodal machine learning.Nature communications · 2025Article
- Exploring the chemical design space of metal-organic frameworks for photocatalysis.Chemical science · 2025Article
- Structural Insights into the Mechanical Behavior of Large-Area 2D Covalent Organic Framework Nanofilms.ACS applied materials & interfaces · 2025Article
- Data-Driven Search Algorithm for Discovery of Synthesizable Zeolitic Imidazolate Frameworks.JACS Au · 2025Article
- The COF Space: Materials Features, Gas Adsorption, and Separation Performances Assessed by Machine Learning.ACS materials letters · 2025Article
- Transitioning metal-organic frameworks from the laboratory to market through applied research.Nature materials · 2025Review
- Machine Learning in Computational Design and Optimization of Disordered Nanoporous Materials.Materials (Basel, Switzerland) · 2025Review
- Deep learning-based recommendation system for metal-organic frameworks (MOFs).Digital discovery · 2024Article
- Expanding the Horizons of Machine Learning in Nanomaterials to Chiral Nanostructures.Advanced materials (Deerfield Beach, Fla.) · 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 at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Finding the best material for a specific application is the ultimate goal of materials discovery. However, there is also the reverse problem: when experimental groups discover a new material, they would like to know all the possible applications this material would be promising for. Computational modeling can aim to fulfill this expectation, thanks to the sustained growth of computing power and the collective engagement of the scientific community in developing more efficient and accurate workflows for predicting materials' performances. We discuss the impact that reproducibility and automation of the modeling protocols have on the field of gas adsorption in nanoporous crystals. We envision a platform that combines these tools and enables effective matching between promising materials and industrial applications.
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.