ReviewNature materials2021
Discovering and understanding materials through computation.
Review in Nature materials, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 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
24 citing papers in PubMed.
- Cross-material catalyst discovery via deep learning.Nature materials · 2026Article
- A manufacturability-informed topology framework for AI-guided design of fibrous network materials.Nature communications · 2026Article
- Generalised quantum computational spectroscopy on a quantum chip.Nature communications · 2026Article
- Navigating polymorph generation and distilled-potential development via entropy-symmetry landscapes for metal plasticity mechanisms.Nature communications · 2026Article
- Moiré excitons in generalized Wigner crystals.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Physics-informed Hamiltonian learning for large-scale optoelectronic property prediction.Nature communications · 2026Article
- An integrated physics-guided machine learning approach for predicting asphalt concrete fracture parameters.Scientific reports · 2026Article
- Impact of Electrostatic Disorder on Intramolecular Electronic Coupling in Organic Mixed Ionic-Electronic Conductors: A Combined GRRM, MD, and QM/MM-CDFT Study.Molecules (Basel, Switzerland) · 2026Article
- In Silico Models for Predicting Adsorption of Organic Pollutants on Atmospheric Nanoplastics by Combining Grand Canonical Monte Carlo/Density Functional Theory and Quantitative Structure Activity Relationship Approach.Nanomaterials (Basel, Switzerland) · 2026Article
- A multimodal dataset of causal mechanisms in materials science literature.Scientific data · 2026Article
- Cross-disciplinary perspectives on the potential for artificial intelligence across chemistry.Chemical Society reviews · 2025Review
- PAH101: A GW+BSE Dataset of 101 Polycyclic Aromatic Hydrocarbon (PAH) Molecular Crystals.Scientific data · 2025Article
- The feather's multi-functional structure across nano to macro scales inspires hierarchical design.Journal of the Royal Society, Interface · 2025Review
- Unsupervised representation learning of Kohn-Sham states and consequences for downstream predictions of many-body effects.Nature communications · 2024Article
- Computational Discovery of Intermolecular Singlet Fission Materials Using Many-Body Perturbation Theory.The journal of physical chemistry. C, Nanomaterials and interfaces · 2024Review
- Insights into the DHQ-BN: mechanical, electronic, and optical properties.Scientific reports · 2024Article
- Emerging computational and machine learning methodologies for proton-conducting oxides: materials discovery and fundamental understanding.Science and technology of advanced materials · 2024Review
- Data-Driven Predetermination of Cu Oxidation State in Copper Nanoparticles: Application to the Synthesis by Laser Ablation in Liquid.Journal of the American Chemical Society · 2023Article
- Accelerating the Design of High-Energy-Density Hydrocarbon Fuels by Learning from the Data.Molecules (Basel, Switzerland) · 2023Article
- On the mechanical, electronic, and optical properties of the boron nitride analog for the recently synthesized biphenylene network: a DFT study.Journal of molecular modeling · 2023Article
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
Abstract
Materials modelling and design using computational quantum and classical approaches is by now well established as an essential pillar in condensed matter physics, chemistry and materials science research, in addition to experiments and analytical theories. The past few decades have witnessed tremendous advances in methodology development and applications to understand and predict the ground-state, excited-state and dynamical properties of materials, ranging from molecules to nanoscopic/mesoscopic materials to bulk and reduced-dimensional systems. This issue of Nature Materials presents four in-depth Review Articles on the field. This Perspective aims to give a brief overview of the progress, as well as provide some comments on future challenges and opportunities. We envision that increasingly powerful and versatile computational approaches, coupled with new conceptual understandings and the growth of techniques such as machine learning, will play a guiding role in the future search and discovery of materials for science and technology.
Identifiers
34045702What 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.