ArticleChemical science2020
Benchmarking the acceleration of materials discovery by sequential learning.
Article in Chemical science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 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
22 citing papers in PubMed.
- Data-driven interfacial regulations through molecular additive screening for batteries and electrocatalysis.Chemical science · 2026Review
- Article
- AI for Accelerated Materials Discovery: From Generative Design to Autonomous Realization.Chemical reviews · 2026Article
- Explicitly Incorporating Prior Knowledge into Bayesian Optimization for Materials Design.ACS omega · 2026Article
- Deciphering key factors of active learning performance in biomolecular design.Bioinformatics (Oxford, England) · 2026Article
- Artificial Intelligence-Powered Raman Spectroscopy through Open Science and FAIR Principles.ACS nano · 2025Review
- Developing machine learning for heterogeneous catalysis with experimental and computational data.Nature reviews. Chemistry · 2025Review
- Bayesian Optimization in Bioprocess Engineering-Where Do We Stand Today?Biotechnology and bioengineering · 2025Review
- Data selection strategies for minimizing measurement time in materials characterization.Scientific reports · 2025Article
- Performance Prediction of High-Entropy Perovskites LaAdvanced materials (Deerfield Beach, Fla.) · 2024Article
- Self-Driving Laboratories for Chemistry and Materials Science.Chemical reviews · 2024Review
- Traditional or adaptive design of experiments? A pilot-scale comparison on wood delignification.Heliyon · 2024Article
- Exploiting redundancy in large materials datasets for efficient machine learning with less data.Nature communications · 2023Article
- Active learning for prediction of tensile properties for material extrusion additive manufacturing.Scientific reports · 2023Article
- Benchmarking AutoML for regression tasks on small tabular data in materials design.Scientific reports · 2022Article
- Autonomous optimization of non-aqueous Li-ion battery electrolytes via robotic experimentation and machine learning coupling.Nature communications · 2022Article
- Agents for sequential learning using multiple-fidelity data.Scientific reports · 2022Article
- A self-driving laboratory advances the Pareto front for material properties.Nature communications · 2022Article
- Routescore: Punching the Ticket to More Efficient Materials Development.ACS central science · 2022Article
- Golem: an algorithm for robust experiment and process optimization.Chemical science · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Sequential learning (SL) strategies,
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.