ArticleScientific data2017
Machine-learned and codified synthesis parameters of oxide materials.
Article in Scientific data, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 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
34 citing papers in PubMed.
- Mechanical performance dataset for alloy with applications at low temperatures.Scientific data · 2025Article
- How to accelerate the inorganic materials synthesis: from computational guidelines to data-driven method?National science review · 2025Review
- Life Cycle Inventory Availability: Status and Prospects for Leveraging New Technologies.ACS sustainable chemistry & engineering · 2024Article
- Supervised Machine Learning-Based Prediction of Hydrogen Storage Classes Utilizing Dibenzyltoluene as an Organic Carrier.Molecules (Basel, Switzerland) · 2024Article
- Text Mining the Literature to Inform Experiments and Rationalize Impurity Phase Formation for BiFeOChemistry of materials : a publication of the American Chemical Society · 2024Article
- Snowball 2.0: Generic Material Data Parser for ChemDataExtractor.Journal of chemical information and modeling · 2023Article
- EXSCLAIM!: Harnessing materials science literature for self-labeled microscopy datasets.Patterns (New York, N.Y.) · 2023Article
- A Model Ensemble Approach Enables Data-Driven Property Prediction for Chemically Deconstructable Thermosets in the Low-Data Regime.ACS central science · 2023Article
- Transcription between human-readable synthetic descriptions and machine-executable instructions: an application of the latest pre-training technology.Chemical science · 2023Article
- Fatigue database of complex metallic alloys.Scientific data · 2023Article
- Precursor recommendation for inorganic synthesis by machine learning materials similarity from scientific literature.Science advances · 2023Article
- Fatigue database of additively manufactured alloys.Scientific data · 2023Article
- Chemistry-Informed Machine Learning for Polymer Electrolyte Discovery.ACS central science · 2023Article
- A rule-free workflow for the automated generation of databases from scientific literature.npj computational materials · 2023Article
- Identify novel elements of knowledge with word embedding.PloS one · 2023Article
- Machine-Learning Rationalization and Prediction of Solid-State Synthesis Conditions.Chemistry of materials : a publication of the American Chemical Society · 2022Article
- Perovskite- and Dye-Sensitized Solar-Cell Device Databases Auto-generated Using ChemDataExtractor.Scientific data · 2022Article
- Dataset of solution-based inorganic materials synthesis procedures extracted from the scientific literature.Scientific data · 2022Article
- Artificial Intelligence for Autonomous Molecular Design: A Perspective.Molecules (Basel, Switzerland) · 2021Review
- Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems.Chemical reviews · 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
Predictive materials design has rapidly accelerated in recent years with the advent of large-scale resources, such as materials structure and property databases generated by ab initio computations. In the absence of analogous ab initio frameworks for materials synthesis, high-throughput and machine learning techniques have recently been harnessed to generate synthesis strategies for select materials of interest. Still, a community-accessible, autonomously-compiled synthesis planning resource which spans across materials systems has not yet been developed. In this work, we present a collection of aggregated synthesis parameters computed using the text contained within over 640,000 journal articles using state-of-the-art natural language processing and machine learning techniques. We provide a dataset of synthesis parameters, compiled autonomously across 30 different oxide systems, in a format optimized for planning novel syntheses of materials.
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.