ReviewNature materials2026
Artificial intelligence-driven approaches for materials design and discovery.
Review in Nature materials, 2026. 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
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
28 citing papers in PubMed.
- Artificial Intelligence-Driven Inverse Design of Singlet Fission Candidates in the Acene family.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Peukert-Informed Prediction and Screening of Manganese-Based High-Rate Potassium Cathode.Small methods · 2026Article
- Mechanistically Interpretable Artificial Intelligence for Designing Oxygen Electrocatalysts.Advanced materials (Deerfield Beach, Fla.) · 2026Article
- Emerging Multimodal Artificial Sensory Fusion Paradigms Toward Advanced Embodied Intelligence.Advanced materials (Deerfield Beach, Fla.) · 2026Review
- Multi-objective and cross-scale inverse design of temperature-control materials via physics-constrained machine learning.National science review · 2026Article
- De Novo-Designed Peptide-Engineered Multimodal Platform for Post-Ischemic Stroke Tissue Repair.Advanced materials (Deerfield Beach, Fla.) · 2026Article
- Decoding non-fullerene acceptors for organic solar cells: a theoretical structure-property-performance perspective.RSC advances · 2026Review
- Physics-Aware Machine-Learning-Driven Inverse Design of Broadband Ultra-Open Acoustic Metamaterials.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Dynamic Kinetics of MgH2 in Solid-State Hydrogen Storage: From the "Dam-Break Effect" to Mechanistic Understanding, Data-Driven Design, and Application Strategies.Accounts of chemical research · 2026Article
- Biomacromolecule-MOF Composites for Intracellular Delivery: From Empirical Construction to Rational Design and AI-Assisted Screening.Small (Weinheim an der Bergstrasse, Germany) · 2026Review
- Illuminating the Interface: In Situ Optical Insights Into Two-Electron ORR Pathways for HAdvanced materials (Deerfield Beach, Fla.) · 2026Review
- Tunable Surface Chemistry of Molten Salt Derived MXenes for Data-Driven Electrochemical Materials Discovery.Small (Weinheim an der Bergstrasse, Germany) · 2026Review
- An exciting Approach to Theoretical Spectroscopy.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Green and Low-Carbon Synthesis of Lithium Sulfide (LiAdvanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- A Problem-Setup-Centric Framework for Mixed-Variable Chemical Formulation Optimization with Customized Constraints.Materials (Basel, Switzerland) · 2026Article
- CCVAN Leverages Conditional Molecular Generation Through Conditional VAE and Wasserstein GAN.Interdisciplinary sciences, computational life sciences · 2026Article
- Spectroscopy-informed XANES-PXRD framework for multi-property prediction and structure inference.Chemical science · 2026Article
- Data-Driven Engineering of Antimicrobial Nanomaterials for Food Safety and Biomedical Systems.Nanomaterials (Basel, Switzerland) · 2026Review
- Reactive Polymeric Membranes for Advanced Water Treatment: Bridging Mechanisms, Kinetics and Scalable Deployment.Polymers · 2026Review
- Building with Light: Photoactive Colloids as the Next Generation Meta-Atoms.Nano letters · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Materials design is an important component of modern science and technology, yet traditional approaches rely heavily on trial and error and can be inefficient. Computational techniques, enhanced by modern artificial intelligence, have reshaped the landscape of designing new materials. Among these approaches, inverse design has shown great promise in designing materials that meet specific property requirements. In this Review, we present key computational advances in materials design over the past few decades. We follow the evolution of relevant materials design techniques, from high-throughput forward machine learning methods and evolutionary algorithms, to advanced artificial intelligence strategies such as reinforcement learning and deep generative models. We highlight the paradigm shift from conventional screening approaches to inverse generation driven by deep generative models. Finally, we discuss current challenges and future perspectives of materials inverse design. This Review may serve as a brief guide to the approaches, progress and outlook of designing future functional materials with technological relevance.
Identifiers
41482571What 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.