ArticleNational science review2023
Data quantity governance for machine learning in materials science.
Article in National science review, 2023. 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.
- A Physical Adaptive Material Motor Unit Neural Network: A Hygromorph Composite Material Machine.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Leveraging Machine Learning for Accelerated Electrode-Electrolyte Interface Design in Rechargeable Li-Based Batteries.Small (Weinheim an der Bergstrasse, Germany) · 2026Review
- Intelligent Computing for Future Layered Cathodes in Sodium-Ion Batteries.Small (Weinheim an der Bergstrasse, Germany) · 2026Review
- From Experimental Optimization to AI Empowerment: Advanced Strategies of Oxygen Evolution Reaction for Large-Scale PEMWE.Small (Weinheim an der Bergstrasse, Germany) · 2026Review
- Adaptive Reduction of Multicollinearity for Machine Learning Prediction of CO Adsorption Energies on Single Atoms.Precision chemistry · 2026Article
- The Influence of Data Clustering on the Generalization Properties of Neural Networks Using the Example of Determining the Geographical Origin of Wine.Foods (Basel, Switzerland) · 2026Article
- Machine Learning and Artificial Intelligence in Metallic Orthopedic Implant Development: A Narrative Review.Materials (Basel, Switzerland) · 2026Review
- Advancing battery failure diagnosis by knowledge-augmented large language models.National science review · 2026Article
- Peroxidase-Mimicking Nanozymes for Rapid Detection of Infectious Diseases.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Towards a sustainable high-quality materials data ecosystem: frameworks and strategies.National science review · 2026Review
- AI-enabled prediction and transformation analysis of phases in multi-component nitride thin films and coatings.iScience · 2026Article
- Leveraging molecular descriptors and explainable machine learning for monomer conversion prediction in photoinduced electron transfer-reversible addition-fragmentation chain transfer polymerization.Scientific reports · 2026Article
- The Evolution of Modeling Approaches: From Statistical Models to Deep Learning for Locust and Grasshopper Forecasting.Insects · 2026Review
- Review
- Superionic Ionic Conductor Discovery via Multiscale Topological Learning.Journal of the American Chemical Society · 2025Article
- Progress in Modeling and Applications of Solid Electrolyte Interphase Layers for Lithium Metal Anodes.Nanomaterials (Basel, Switzerland) · 2025Review
- Proteomics and machine learning: Leveraging domain knowledge for feature selection in a skeletal muscle tissue meta-analysis.Heliyon · 2024Article
- Exploring the Phase Stability of LiACS omega · 2024Article
- ChatMOF: an artificial intelligence system for predicting and generating metal-organic frameworks using large language models.Nature communications · 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
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Data-driven machine learning (ML) is widely employed in the analysis of materials structure-activity relationships, performance optimization and materials design due to its superior ability to reveal latent data patterns and make accurate prediction. However, because of the laborious process of materials data acquisition, ML models encounter the issue of the mismatch between a high dimension of feature space and a small sample size (for traditional ML models) or the mismatch between model parameters and sample size (for deep-learning models), usually resulting in terrible performance. Here, we review the efforts for tackling this issue via feature reduction, sample augmentation and specific ML approaches, and show that the balance between the number of samples and features or model parameters should attract great attention during data quantity governance. Following this, we propose a synergistic data quantity governance flow with the incorporation of materials domain knowledge. After summarizing the approaches to incorporating materials domain knowledge into the process of ML, we provide examples of incorporating domain knowledge into governance schemes to demonstrate the advantages of the approach and applications. The work paves the way for obtaining the required high-quality data to accelerate materials design and discovery based on ML.
Indexed as
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.