ReviewNature2018
Machine learning for molecular and materials science.
Review in Nature, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 752 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
752 citing papers in PubMed.
- Artificial intelligence-guided nanozyme engineering for chronic wound healing: from rational design to precision therapeutics.Bioactive materials · 2027Review
- Molecular Rectification: Mechanisms, Modulation Strategies, and Device Realization.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Artificial Intelligence in the Discovery and Design of Ionic Liquids for Thermal Energy Applications.Journal of chemical information and modeling · 2026Review
- Critical review of adsorption technologies for heavy metal removal: mechanistic, computational, and policy perspectives.RSC advances · 2026Review
- Quantum Computing for Polymeric Composites: Principles, Methods, Applications, and Future Perspectives.Polymers · 2026Review
- Injectable Thermoresponsive Hydrogels for Antibacterial Applications: Design Strategies, Antibacterial Mechanisms, and Biomedical Applications.Materials (Basel, Switzerland) · 2026Review
- Soft Computing Approaches toward Density-Based Analysis of Many-Electron Systems.ACS physical chemistry Au · 2026Review
- Reasoning-Driven Design of Single-Atom Catalysts via a Multiagent Large Language Model Framework.ACS central science · 2026Article
- Scalable molecular representations enabled by multimodal fusion and sequence distillation.Nature communications · 2026Article
- Reliable machine learning deployment across domains in electrochemical energy storage materials.iScience · 2026Article
- Spatiotemporal all-optical diffractive neural networks empowered by spatiotemporal light field manipulation.Light, science & applications · 2026Article
- Data-driven interfacial regulations through molecular additive screening for batteries and electrocatalysis.Chemical science · 2026Review
- Harnessing Selectivity in COACS environmental Au · 2026Review
- Chemically Interpretable Explanations for Molecular Property Prediction via Fragment-Level Shapley Values.Journal of chemical information and modeling · 2026Article
- Deep learning directed synthesis of fluid ferroelectric materials.Materials horizons · 2026Article
- A‑iMPO: A Design-Time Prediction Toolbox for Blood Brain Barrier Permeation Using an Integrated and Interpretable Multiparameter Optimization Framework.ACS medicinal chemistry letters · 2026Article
- A Physics-Informed Machine Learning Framework for Modeling Biaxial Strain-Induced Band Gap Variation in Zig-Zag Single-Walled Carbon Nanotubes.Nanomaterials (Basel, Switzerland) · 2026Article
- Leveraging Machine Learning for Accelerated Electrode-Electrolyte Interface Design in Rechargeable Li-Based Batteries.Small (Weinheim an der Bergstrasse, Germany) · 2026Review
- Article
- Review
692 more citing papers are in PubMed but not listed here.
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
No grant is acknowledged in the PubMed record.
Abstract
Here we summarize recent progress in machine learning for the chemical sciences. We outline machine-learning techniques that are suitable for addressing research questions in this domain, as well as future directions for the field. We envisage a future in which the design, synthesis, characterization and application of molecules and materials is accelerated by artificial intelligence.
Identifiers
30046072What 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.