ReviewChemical science2026
Digital materials ecosystem: from databases to AI agents for autonomous discovery.
Review in Chemical science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- Pre-catalysts for the hydrogen evolution reaction: from dynamic reconstruction to rational design.Chemical science · 2026Review
- Materials Databases: Foundations of Modern Digital Materials.Precision chemistry · 2026Review
- Balanced Electron Donation Governs Adsorption Site Selection in CO2 Electroreduction to HCOOH on Transition Metal-Doped Magnesium Oxides.The journal of physical chemistry letters · 2026Article
- A unified descriptor framework for hydrogen storage capacity and equilibrium pressure in interstitial hydrides.Chemical science · 2026Article
- Data-Driven Engineering of Antimicrobial Nanomaterials for Food Safety and Biomedical Systems.Nanomaterials (Basel, Switzerland) · 2026Review
- Introduction to high throughput synthesis, characterisation and optimisation of nanomaterials.Nanoscale advances · 2026Article
- Transforming Nanomaterials Development with Artificial Intelligence Techniques.Nanotechnology, science and applications · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The concept of a digital materials ecosystem represents a new paradigm in materials research, where data, theory, and automation are integrated into a unified and iterative framework. By combining reliable databases, physical frameworks, and intelligent data analysis, materials discovery is evolving from empirical exploration toward a systematic and predictive science. The rapid growth of data and artificial intelligence (AI) has enabled the identification of complex structure-property relationships, while advances in automated synthesis and high-throughput characterization are closing the loop between prediction and validation. Looking forward, the field must focus on building trustworthy and benchmarked datasets, developing interpretable and high-precision models, and designing AI tools that embody human scientific reasoning. Equally important is ensuring standardization and consistency between digital inputs and experimental responses. Together, these efforts will transform materials discovery from data accumulation into genuine knowledge generation, paving the way for an autonomous and self-improving research ecosystem that accelerates both fundamental understanding and technological 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.