ReviewAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2024
When Machine Learning Meets 2D Materials: A Review.
Review in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 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
21 citing papers in PubMed.
- Uncertainty-Aware Machine Learning for Compact-Model-Based Design and Identifiability Analysis of WSeMicromachines · 2026Article
- Data-Efficient Machine Learning for Predicting Dopant Formation Energies in TiOACS materials Au · 2026Article
- Engineering two-dimensional electronic biosensors.Nature reviews electrical engineering · 2026Article
- ReflectoRNN: AI-Enabled In-Operando Optical Reflectometry for Evolving Materials Using a Recurrent Neural Network.ACS nano · 2026Article
- Progress in Strain Engineering of 2D-Integrated Heterostructures for Ultrasensitive Sensors.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Application of Machine Learning in Predicting the Properties of Two-Dimensional Semiconductor Materials.Nanomaterials (Basel, Switzerland) · 2026Review
- 2D Nanomaterials Toward Function-Ready Superlubricity in Advanced Microsystems.Advanced materials (Deerfield Beach, Fla.) · 2026Review
- Recent Advances in Sustainable Single-Atom Catalysts from Biomass and Solid Waste: Design, Synthesis and Applications.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- The Promise of Solid Lubricants for a Sustainable Future.Advanced materials (Deerfield Beach, Fla.) · 2026Article
- Deep learning methods for 2D material electronic properties.Digital discovery · 2026Review
- Artificial Intelligence Empowered New Materials: Discovery, Synthesis, Prediction to Validation.Nano-micro letters · 2026Review
- Characterization of the 3D-Optical Properties of van der Waals Materials with Deep Learning-Based Coherent Fourier Scatterometry.ACS photonics · 2026Article
- Bubbles in 2D Materials: Formation Mechanisms, Impacts, and Removal Strategies for Next-Generation Electronic Devices.Nanomaterials (Basel, Switzerland) · 2025Review
- Controlled Growth of Graphene-Skinned AlAdvanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Computational Approaches for Designing Heterostructured Electrocatalysts.Small science · 2025Review
- Progress in Modeling and Applications of Solid Electrolyte Interphase Layers for Lithium Metal Anodes.Nanomaterials (Basel, Switzerland) · 2025Review
- TiMolecules (Basel, Switzerland) · 2025Review
- Review
- Review
- MXenes and artificial intelligence: fostering advancements in synthesis techniques and breakthroughs in applications.RSC advances · 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
11 authors.
Funding
Abstract
The availability of an ever-expanding portfolio of 2D materials with rich internal degrees of freedom (spin, excitonic, valley, sublattice, and layer pseudospin) together with the unique ability to tailor heterostructures made layer by layer in a precisely chosen stacking sequence and relative crystallographic alignments, offers an unprecedented platform for realizing materials by design. However, the breadth of multi-dimensional parameter space and massive data sets involved is emblematic of complex, resource-intensive experimentation, which not only challenges the current state of the art but also renders exhaustive sampling untenable. To this end, machine learning, a very powerful data-driven approach and subset of artificial intelligence, is a potential game-changer, enabling a cheaper - yet more efficient - alternative to traditional computational strategies. It is also a new paradigm for autonomous experimentation for accelerated discovery and machine-assisted design of functional 2D materials and heterostructures. Here, the study reviews the recent progress and challenges of such endeavors, and highlight various emerging opportunities in this frontier research area.
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.