ArticleMRS communications2019
Materials Science in the AI age: high-throughput library generation, machine learning and a pathway from correlations to the underpinning physics.
Article in MRS communications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 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.
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Who cites it
28 citing papers in PubMed.
- Artificial Intelligence-Assisted Workflow for Transmission Electron Microscopy: From Data Analysis Automation to Materials Knowledge Unveiling.Advanced materials (Deerfield Beach, Fla.) · 2026Article
- Machine Learning for Structural Steels: Materials Design, Property Prediction, Durability, and Future Directions.Materials (Basel, Switzerland) · 2026Review
- CHIPS-TB: Evaluating Tight-Binding Models for Metals, Semiconductors, and Insulators.The journal of physical chemistry. C, Nanomaterials and interfaces · 2026Article
- Review
- MicroscopyGPT: Generating Atomic-Structure Captions from Microscopy Images of 2D Materials with Vision-Language Transformers.The journal of physical chemistry letters · 2025Article
- Automated image segmentation for accelerated nanoparticle characterization.Scientific reports · 2025Article
- DiffractGPT: Atomic Structure Determination from X-ray Diffraction Patterns Using a Generative Pretrained Transformer.The journal of physical chemistry letters · 2025Article
- Article
- First-principles data for solid solution niobium-tantalum-vanadium alloys with body-centered-cubic structures.Scientific data · 2024Article
- Theoretical Prediction of Thermal Expansion Anisotropy for YMaterials (Basel, Switzerland) · 2024Article
- Applying machine learning to assess the morphology of sculpted teeth.Journal of dental sciences · 2024Article
- The rule of four: anomalous distributions in the stoichiometries of inorganic compounds.npj computational materials · 2024Article
- Exploiting redundancy in large materials datasets for efficient machine learning with less data.Nature communications · 2023Article
- Fast and accurate prediction of material properties with three-body tight-binding model for the periodic table.Physical review materials · 2023Article
- Autonomous scanning probe microscopy with hypothesis learning: Exploring the physics of domain switching in ferroelectric materials.Patterns (New York, N.Y.) · 2023Article
- Emerging triboelectric nanogenerators for the prevention and monitoring of inflammation.Frontiers in immunology · 2023Article
- A comparison of explainable artificial intelligence methods in the phase classification of multi-principal element alloys.Scientific reports · 2022Article
- Universal machine learning framework for defect predictions in zinc blende semiconductors.Patterns (New York, N.Y.) · 2022Article
- Correlative imaging of ferroelectric domain walls.Scientific reports · 2022Article
- The materials tetrahedron has a "digital twin".MRS bulletin · 2022Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
The use of advanced data analytics and applications of statistical and machine learning approaches ('AI') to materials science is experiencing explosive growth recently. In this prospective, we review recent work focusing on generation and application of libraries from both experiment and theoretical tools, across length scales. The available library data both enables classical correlative machine learning, and also opens the pathway for exploration of underlying causative physical behaviors. We highlight the key advances facilitated by this approach, and illustrate how modeling, macroscopic experiments and atomic-scale imaging can be combined to dramatically accelerate understanding and development of new material systems via a statistical physics framework. These developments point towards a data driven future wherein knowledge can be aggregated and used collectively, accelerating the advancement of materials science.
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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.