mlsanityData to 14 Oct 2026

Machine Learning for a Sustainable Energy Future

arXiv 2210.10391 ↗First posted 19 Oct 2022cond-mat.mtrl-sci cs.LGcited by 14

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Abstract (arXiv)

Transitioning from fossil fuels to renewable energy sources is a critical global challenge; it demands advances at the levels of materials, devices, and systems for the efficient harvesting, storage, conversion, and management of renewable energy. Researchers globally have begun incorporating machine learning (ML) techniques with the aim of accelerating these advances. ML technologies leverage statistical trends in data to build models for prediction of material properties, generation of candidate structures, optimization of processes, among other uses; as a result, they can be incorporated into discovery and development pipelines to accelerate progress. Here we review recent advances in ML-driven energy research, outline current and future challenges, and describe what is required moving forward to best lever ML techniques. To start, we give an overview of key ML concepts. We then introduce a set of key performance indicators to help 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 outlook of potential research areas in the energy field that stand to further benefit from the application of ML.

Key facts. Cited by 14 papers in the map (data through 14 Oct 2026). Builds on 29 of its 200 references that are in the map. First posted on arXiv 19 Oct 2022.

Builds on

29 of 200 references.

References · 29

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17 more
  1. 2018Junction Tree Variational Autoencoder for Molecular Graph Generationcited by 652
  2. 2020Underspecification Presents Challenges for Credibility in Modern Machine Learningcited by 496
  3. 2020Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awarenesscited by 389
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 3; 2024: 4; 2025: 5; 2026: 2.

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  1. 2024Towards Urban General Intelligence: A Review and Outlook of Urban Foundation Modelscited by 23
  2. 2025ChemPile: A 250GB Diverse and Curated Dataset for Chemical Foundation Modelscited by 9
  3. 2024Implicitly Guided Design with PropEn: Match your Data to Follow the Gradientcited by 4
  4. 2024Reinforcement Learning for Sustainable Energy: A Surveycited by 2
  5. 2023A Comprehensive Study of Groundbreaking Machine Learning Research: Analyzing highly cited and impactful publications across six decadescited by 1
  6. 2023Differentiable Modeling and Optimization of Battery Electrolyte Mixtures Using Geometric Deep Learningcited by 1
  7. 2023Accurate battery lifetime prediction across diverse aging conditions with deep learningcited by 1
  8. 2024A Generation Framework with Strict Constraints for Crystal Materials Designcited by 1
  9. 2025Machine learning for in-situ composition mapping in a self-driving magnetron sputtering systemcited by 1
  10. 2025Engineering Artificial Intelligence: Framework, Challenges, and Future Directioncited by 0
  11. 2025Machine Learning Reveals Composition Dependent Thermal Stability in Halide Perovskitescited by 0
  12. 2025Deep Learning in Renewable Energy Forecasting: A Cross-Dataset Evaluation of Temporal and Spatial Modelscited by 0
2 more
  1. 2026CheMLFlow: An Open-Source Platform for Cheminformatics and Materials Informatics Applicationscited by 0
  2. 2026SCALE: Simulation-Calibrated Amortized Learning for Energy Materials (A hybrid architecture connecting deterministic modeling, real-world data, and transformer-scale inference for accelerated energy-materials discovery)cited by 0