ReviewMaterials (Basel, Switzerland)2023
Unleashing the Power of Artificial Intelligence in Materials Design.
Review in Materials (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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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.
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Who cites it
18 citing papers in PubMed.
- Next generation materials for environmental remediation through advanced mechanisms and future perspectives.Discover nano · 2026Review
- An integrated physics-guided machine learning approach for predicting asphalt concrete fracture parameters.Scientific reports · 2026Article
- Ultra-Short Peptide Hydrogels as 3D Bioprinting Materials.Gels (Basel, Switzerland) · 2026Review
- From Pixels to Predictions: Integrating Machine Learning and Digital Image Correlation for Damage Identification in Engineering Materials.Materials (Basel, Switzerland) · 2025Review
- The northeast materials database for magnetic materials.Nature communications · 2025Article
- Prediction of Mechanical Properties and Stress-Strain Relation of Closed-Cell Aluminium Foam Under Compression Using Neural Network Models.Materials (Basel, Switzerland) · 2025Article
- Iron-Based High-Temperature Alloys: Alloying Strategies and New Opportunities.Materials (Basel, Switzerland) · 2025Review
- Review
- The ethics of using artificial intelligence in scientific research: new guidance needed for a new tool.AI and ethics · 2025Article
- Convergence of Nanotechnology and Machine Learning: The State of the Art, Challenges, and Perspectives.International journal of molecular sciences · 2024Review
- Data-Driven Bi-Directional Lattice Property Customization and Optimization.Materials (Basel, Switzerland) · 2024Article
- Three-Dimensionally-Printed Polymer and Composite Materials for Dental Applications with Focus on Orthodontics.Polymers · 2024Review
- Design of Shape Forming Elements for Architected Composites via Bayesian Optimization and Genetic Algorithms: A Concept Evaluation.Materials (Basel, Switzerland) · 2024Article
- Advanced Computational Methods for Modeling, Prediction and Optimization-A Review.Materials (Basel, Switzerland) · 2024Review
- Achieving Endo/Lysosomal Escape Using Smart Nanosystems for Efficient Cellular Delivery.Molecules (Basel, Switzerland) · 2024Review
- AI-Based Metamaterial Design.ACS applied materials & interfaces · 2024Review
- Estimation of concrete materials uniaxial compressive strength using soft computing techniques.Heliyon · 2023Article
- Recent Advances in the Development of Biomimetic Materials.Gels (Basel, Switzerland) · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The integration of artificial intelligence (AI) algorithms in materials design is revolutionizing the field of materials engineering thanks to their power to predict material properties, design de novo materials with enhanced features, and discover new mechanisms beyond intuition. In addition, they can be used to infer complex design principles and identify high-quality candidates more rapidly than trial-and-error experimentation. From this perspective, herein we describe how these tools can enable the acceleration and enrichment of each stage of the discovery cycle of novel materials with optimized properties. We begin by outlining the state-of-the-art AI models in materials design, including machine learning (ML), deep learning, and materials informatics tools. These methodologies enable the extraction of meaningful information from vast amounts of data, enabling researchers to uncover complex correlations and patterns within material properties, structures, and compositions. Next, a comprehensive overview of AI-driven materials design is provided and its potential future prospects are highlighted. By leveraging such AI algorithms, researchers can efficiently search and analyze databases containing a wide range of material properties, enabling the identification of promising candidates for specific applications. This capability has profound implications across various industries, from drug development to energy storage, where materials performance is crucial. Ultimately, AI-based approaches are poised to revolutionize our understanding and design of materials, ushering in a new era of accelerated innovation and advancement.
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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.