ReviewDiscover materials2021
Big data and machine learning for materials science.
Review in Discover materials, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 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
23 citing papers in PubMed.
- Formation Energy Prediction and Feature Analysis of Lead-Free Double Perovskites Based on Deep Learning.Molecules (Basel, Switzerland) · 2026Article
- Advances and Challenges in Wearable Sensors for Health Monitoring.ACS applied materials & interfaces · 2026Review
- Machine learning in next-generation AEM fuel cells: a systematic review.RSC advances · 2026Review
- The Predictive Power of Chemical Bonding Analysis in Materials: A Perspective on Optoelectronic Properties.Journal of the American Chemical Society · 2025Review
- EcoCurrentNet an integrated DNN-CatBoost model for predicting optoelectronic material performance under varying conditions.Scientific reports · 2025Article
- From molecules to data: the emerging impact of chemoinformatics in chemistry.Journal of cheminformatics · 2025Review
- Enhancing energy predictions in multi-atom systems with multiscale topological learning.Journal of materials chemistry. A · 2025Article
- Transfer Learning-Enhanced Prediction of Glass Transition Temperature in Bismaleimide-Based Polyimides.Polymers · 2025Article
- Machine Learning-Driven Prediction of Composite Materials Properties Based on Experimental Testing Data.Polymers · 2025Article
- Semi-automatic construction of heterogeneous data schema based on structure and context-aware recommendation.Scientific data · 2025Article
- From Sunlight to Solutions: Closing the Loop on Halide Perovskites.ACS materials Au · 2025Review
- Machine learning interatomic potential: Bridge the gap between small-scale models and realistic device-scale simulations.iScience · 2024Review
- Classification of battery compounds using structure-free Mendeleev encodings.Journal of cheminformatics · 2024Article
- Machine-Guided Discovery of Acrylate Photopolymer Compositions.ACS applied materials & interfaces · 2024Article
- A big data analysis algorithm for massive sensor medical images.PeerJ. Computer science · 2024Article
- Analysis of Unconfined Compressive Strength of Rammed Earth Mixes Based on Artificial Neural Network and Statistical Analysis.Materials (Basel, Switzerland) · 2022Article
- Materials discovery of ion-selective membranes using artificial intelligence.Communications chemistry · 2022Review
- Digital Innovation Enabled Nanomaterial Manufacturing; Machine Learning Strategies and Green Perspectives.Nanomaterials (Basel, Switzerland) · 2022Review
- High-throughput inverse design and Bayesian optimization of functionalities: spin splitting in two-dimensional compounds.Scientific data · 2022Article
- Tailoring Vibrational Signature and Functionality of 2D-Ordered Linear-Chain Carbon-Based Nanocarriers for Predictive Performance Enhancement of High-End Energetic Materials.Nanomaterials (Basel, Switzerland) · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Herein, we review aspects of leading-edge research and innovation in materials science that exploit big data and machine learning (ML), two computer science concepts that combine to yield computational intelligence. ML can accelerate the solution of intricate chemical problems and even solve problems that otherwise would not be tractable. However, the potential benefits of ML come at the cost of big data production; that is, the algorithms demand large volumes of data of various natures and from different sources, from material properties to sensor data. In the survey, we propose a roadmap for future developments with emphasis on computer-aided discovery of new materials and analysis of chemical sensing compounds, both prominent research fields for ML in the context of materials science. In addition to providing an overview of recent advances, we elaborate upon the conceptual and practical limitations of big data and ML applied to materials science, outlining processes, discussing pitfalls, and reviewing cases of success and failure.
Indexed as
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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.