# Machine Learning for a Sustainable Energy Future

arXiv: https://arxiv.org/abs/2210.10391 · HTML: https://mlsanity.com/paper/2210.10391/

## 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 (most cited first, 29 of 29)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles](https://mlsanity.com/paper/1612.01474/) (2016)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Relational inductive biases, deep learning, and graph networks](https://mlsanity.com/paper/1806.01261/) (2018)
- [E(n) Equivariant Graph Neural Networks](https://mlsanity.com/paper/2102.09844/) (2021)
- [Meta-Learning in Neural Networks: A Survey](https://mlsanity.com/paper/2004.05439/) (2020)
- [An Overview of Multi-Task Learning in Deep Neural Networks](https://mlsanity.com/paper/1706.05098/) (2017)
- [Automatic chemical design using a data-driven continuous representation of molecules](https://mlsanity.com/paper/1610.02415/) (2016)
- [Convolutional Networks on Graphs for Learning Molecular Fingerprints](https://mlsanity.com/paper/1509.09292/) (2015)
- [Self-supervised Learning: Generative or Contrastive](https://mlsanity.com/paper/2006.08218/) (2020)
- [Junction Tree Variational Autoencoder for Molecular Graph Generation](https://mlsanity.com/paper/1802.04364/) (2018)
- [Underspecification Presents Challenges for Credibility in Modern Machine Learning](https://mlsanity.com/paper/2011.03395/) (2020)
- [Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness](https://mlsanity.com/paper/2006.10108/) (2020)
- [Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation](https://mlsanity.com/paper/1806.02473/) (2018)
- [Explaining NonLinear Classification Decisions with Deep Taylor Decomposition](https://mlsanity.com/paper/1512.02479/) (2015)
- [Constrained Graph Variational Autoencoders for Molecule Design](https://mlsanity.com/paper/1805.09076/) (2018)
- [Learning to Plan Chemical Syntheses](https://mlsanity.com/paper/1708.04202/) (2017)
- [GraphEBM: Molecular Graph Generation with Energy-Based Models](https://mlsanity.com/paper/2102.00546/) (2021)
- [The AI Economist: Improving Equality and Productivity with AI-Driven Tax Policies](https://mlsanity.com/paper/2004.13332/) (2020)
- [Batch Active Learning Using Determinantal Point Processes](https://mlsanity.com/paper/1906.07975/) (2019)
- [Gaussian process regression for forecasting battery state of health](https://mlsanity.com/paper/1703.05687/) (2017)
- [Building Energy Load Forecasting using Deep Neural Networks](https://mlsanity.com/paper/1610.09460/) (2016)
- [Inorganic Materials Synthesis Planning with Literature-Trained Neural Networks](https://mlsanity.com/paper/1901.00032/) (2018)
- [An unexpected unity among methods for interpreting model predictions](https://mlsanity.com/paper/1611.07478/) (2016)
- [Neural networks for the prediction organic chemistry reactions](https://mlsanity.com/paper/1608.06296/) (2016)
- [Paradigm shift in electron-based crystallography via machine learning](https://mlsanity.com/paper/1902.03682/) (2019)
- [Machine Learning Enabled Computational Screening of Inorganic Solid Electrolytes for Dendrite Suppression with Li Metal Anode](https://mlsanity.com/paper/1804.04651/) (2018)

## Built on by (most cited first, 14 of 14)

- [Towards Urban General Intelligence: A Review and Outlook of Urban Foundation Models](https://mlsanity.com/paper/2402.01749/) (2024)
- [ChemPile: A 250GB Diverse and Curated Dataset for Chemical Foundation Models](https://mlsanity.com/paper/2505.12534/) (2025)
- [Implicitly Guided Design with PropEn: Match your Data to Follow the Gradient](https://mlsanity.com/paper/2405.18075/) (2024)
- [Reinforcement Learning for Sustainable Energy: A Survey](https://mlsanity.com/paper/2407.18597/) (2024)
- [A Comprehensive Study of Groundbreaking Machine Learning Research: Analyzing highly cited and impactful publications across six decades](https://mlsanity.com/paper/2308.00855/) (2023)
- [Differentiable Modeling and Optimization of Battery Electrolyte Mixtures Using Geometric Deep Learning](https://mlsanity.com/paper/2310.03047/) (2023)
- [Accurate battery lifetime prediction across diverse aging conditions with deep learning](https://mlsanity.com/paper/2310.05052/) (2023)
- [A Generation Framework with Strict Constraints for Crystal Materials Design](https://mlsanity.com/paper/2411.08464/) (2024)
- [Machine learning for in-situ composition mapping in a self-driving magnetron sputtering system](https://mlsanity.com/paper/2506.05999/) (2025)
- [Engineering Artificial Intelligence: Framework, Challenges, and Future Direction](https://mlsanity.com/paper/2504.02269/) (2025)
- [Machine Learning Reveals Composition Dependent Thermal Stability in Halide Perovskites](https://mlsanity.com/paper/2504.04002/) (2025)
- [Deep Learning in Renewable Energy Forecasting: A Cross-Dataset Evaluation of Temporal and Spatial Models](https://mlsanity.com/paper/2505.03109/) (2025)
- [CheMLFlow: An Open-Source Platform for Cheminformatics and Materials Informatics Applications](https://mlsanity.com/paper/2608.04942/) (2026)
- [SCALE: 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)](https://mlsanity.com/paper/2609.22233/) (2026)

## 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.