ArticleACS central science2023
An Ecosystem for Digital Reticular Chemistry.
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 10 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
10 citing papers in PubMed.
- Generalized Machine Learning Potentials for Predicting Low-Pressure Water Adsorption in Flexible Al-Based Metal-Organic Frameworks.Journal of chemical theory and computation · 2026Article
- GFN1-xTB-Assisted Machine Learning for Electronic Structure Screening of Metal-Organic Frameworks.Journal of chemical theory and computation · 2026Article
- Oxidative Ring-Opening of Dimethylfuran in Zeolitic Imidazolate Frameworks through Computational Design.The journal of physical chemistry. C, Nanomaterials and interfaces · 2026Article
- Molecular Modeling-Based Machine Learning for Accurate Prediction of Gas Diffusivity and Permeability in Metal-Organic Frameworks.ACS materials Au · 2026Article
- Converging the Complementary Traits of Metal-Organic Frameworks and Covalent Organic Frameworks.ACS applied materials & interfaces · 2025Review
- A Refined Set of Universal Force Field Parameters for Some Metal Nodes in Metal-Organic Frameworks.Journal of chemical theory and computation · 2024Article
- Deep learning-based recommendation system for metal-organic frameworks (MOFs).Digital discovery · 2024Article
- The carbon footprint of predicting COiScience · 2024Article
- Machine learning insights into predicting biogas separation in metal-organic frameworks.Communications chemistry · 2024Article
- MOFormer: Self-Supervised Transformer Model for Metal-Organic Framework Property Prediction.Journal of the American Chemical Society · 2023Article
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
The vastness of the materials design space makes it impractical to explore using traditional brute-force methods, particularly in reticular chemistry. However, machine learning has shown promise in expediting and guiding materials design. Despite numerous successful applications of machine learning to reticular materials, progress in the field has stagnated, possibly because digital chemistry is more an art than a science and its limited accessibility to inexperienced researchers. To address this issue, we present mofdscribe, a software ecosystem tailored to novice and seasoned digital chemists that streamlines the ideation, modeling, and publication process. Though optimized for reticular chemistry, our tools are versatile and can be used in nonreticular materials research. We believe that mofdscribe will enable a more reliable, efficient, and comparable field of digital chemistry.
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.