ReviewNature reviews. Materials2023
Machine learning for a sustainable energy future.
Review in Nature reviews. Materials, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 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
44 citing papers in PubMed, 436 citations in OpenAlex.
- From Experimental Optimization to AI Empowerment: Advanced Strategies of Oxygen Evolution Reaction for Large-Scale PEMWE.Small (Weinheim an der Bergstrasse, Germany) · 2026Review
- Atomistic machine learning with irreducible Cartesian natural tensors.Nature communications · 2026Article
- Molten salt electrochemistry for next-generation batteries.Nature reviews. Chemistry · 2026Review
- Optimization of Metal-Oxide Spectral Filters for Reduced Photovoltaic Heating via Machine-Learning Guided Thickness Design.ACS omega · 2026Article
- High-Accuracy Machine Learning Projections of Composition-Dependent Thermal Stability in Halide Perovskites.Advanced materials (Deerfield Beach, Fla.) · 2026Article
- On-chip electrocatalytic microcells for high-throughput experimentation of electrocatalysts.iScience · 2026Review
- Machine learning-based prediction of biomass pyrolysis kinetics: integrating mechanistic modeling and compositional features.RSC advances · 2026Article
- Sustainable Materials Design With Multi-Modal Artificial Intelligence.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Machine Learning for Designing Perovskites and Perovskite-Inspired Solar Materials: Emerging Opportunities and Challenges.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Deep active learning and knowledge transfer for rapid discovery of lithium metal battery electrolytes.Nature communications · 2026Article
- General-Purpose Models for the Chemical Sciences: LLMs and Beyond.Chemical reviews · 2026Review
- Machine learning in next-generation AEM fuel cells: a systematic review.RSC advances · 2026Review
- Early prediction of lithium-ion battery degradation with a generative pre-trained transformer.Nature communications · 2025Article
- COGEniX: an industrial informatics framework for cognitive energy forecasting and carbon-aware scheduling in smart zones.Scientific reports · 2025Article
- EcoCurrentNet an integrated DNN-CatBoost model for predicting optoelectronic material performance under varying conditions.Scientific reports · 2025Article
- Uncovering battery electrochemical mechanisms by artificial intelligence.National science review · 2025Review
- Dynamics in electrochemical organic oxidation reactions from in situ and operando techniques.Nature reviews. Chemistry · 2025Review
- Bio-hydrovoltaic technology: advancing from non-living to living hydrovoltaic systems.National science review · 2025Review
- 33 Unresolved Questions in Nanoscience and Nanotechnology.ACS nano · 2025Article
- AI-Driven Defect Engineering for Advanced Thermoelectric Materials.Advanced materials (Deerfield Beach, Fla.) · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Transitioning from fossil fuels to renewable energy sources is a critical global challenge; it demands advances - at the materials, devices and systems levels - for the efficient harvesting, storage, conversion and management of renewable energy. Energy researchers have begun to incorporate machine learning (ML) techniques to accelerate these advances. In this Perspective, we highlight recent advances in ML-driven energy research, outline current and future challenges, and describe what is required to make the best use of ML techniques. We introduce a set of key performance indicators with which to compare the benefits of different ML-accelerated workflows for energy research. We discuss and evaluate the latest advances in applying ML to the development of energy harvesting (photovoltaics), storage (batteries), conversion (electrocatalysis) and management (smart grids). Finally, we offer an overview of potential research areas in the energy field that stand to benefit further from the application of ML.
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.