ArticleNature communications2023
Exploiting redundancy in large materials datasets for efficient machine learning with less data.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 19 papers.
What it found
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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
19 citing papers in PubMed.
- Transformers for molecular property prediction: domain adaptation efficiently improves performance.Journal of cheminformatics · 2026Article
- Predicting drug release from polymeric long-acting injectables using a machine learning approach to decode formulation-performance relationships.Drug delivery and translational research · 2026Article
- Machine Learning for Superconductor Discovery: From Data-Driven Insights to Accelerated Design.ACS omega · 2026Review
- A deep learning framework for comprehensive prediction of human RNA G-quadruplex-binding proteins.Bioinformatics (Oxford, England) · 2026Article
- Machine Learning for Neurotransmitter Monitoring by Fast Voltammetry: Current and Future Prospects.ACS chemical neuroscience · 2026Review
- FibreCastML: an open web platform for predicting electrospun nanofibre diameter distributions for biomedical applications.Frontiers in bioengineering and biotechnology · 2026Article
- Multifunctional polyimide performance prediction based on explainable machine learning.Smart molecules : open access · 2025Article
- Hybrid ST-ResNet and LSTM approach for precise crime hotspot prediction.Scientific reports · 2025Article
- A comprehensive benchmark of active learning strategies with AutoML for small-sample regression in materials science.Scientific reports · 2025Article
- Enhancing Reverse Design Ability of Functional Materials Based on Data Quality Management: Taking Biomedical Zinc Alloy as an Example.Materials (Basel, Switzerland) · 2025Article
- From data to discovery: leveraging big data in plant natural products biosynthesis research.The Plant journal : for cell and molecular biology · 2025Review
- Model-free estimation of completeness, uncertainties, and outliers in atomistic machine learning using information theory.Nature communications · 2025Article
- Simultaneous prediction of 10m and 100m wind speeds using a model inspired by brain emotional learning.Scientific reports · 2025Article
- The implementation of digital biomarkers in the diagnosis, treatment and monitoring of mood disorders: a narrative review.Frontiers in digital health · 2025Review
- Article
- Thermal Metamaterials with Configurable Mechanical Properties.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Sample Size Requirements of a Pharmaceutical Material Library: A Case in Predicting Direct Compression Tablet Tensile Strength by Latent Variable Modeling.Pharmaceutics · 2024Article
- Exploiting redundancy in large materials datasets for efficient machine learning with less data.Nature communications · 2023Article
- Artificial Intelligence in Medicine: A Specialty-Level Overview of Emerging AI Trends.JSLS : Journal of the Society of Laparoendoscopic SurgeonsReview
Corrections and comments
- Erratum issued
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Extensive efforts to gather materials data have largely overlooked potential data redundancy. In this study, we present evidence of a significant degree of redundancy across multiple large datasets for various material properties, by revealing that up to 95% of data can be safely removed from machine learning training with little impact on in-distribution prediction performance. The redundant data is related to over-represented material types and does not mitigate the severe performance degradation on out-of-distribution samples. In addition, we show that uncertainty-based active learning algorithms can construct much smaller but equally informative datasets. We discuss the effectiveness of informative data in improving prediction performance and robustness and provide insights into efficient data acquisition and machine learning training. This work challenges the "bigger is better" mentality and calls for attention to the information richness of materials data rather than a narrow emphasis on data volume.
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.