# Machine Learning - Driven Materials Discovery: Unlocking Next-Generation Functional Materials - A review

arXiv: https://arxiv.org/abs/2503.18975 · HTML: https://mlsanity.com/paper/2503.18975/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 254 references that are in the map.
- First posted on arXiv 22 Mar 2025.

## Builds on (most cited first, 16 of 16)

- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials](https://mlsanity.com/paper/2101.03164/) (2021)
- [Quantum Machine Learning](https://mlsanity.com/paper/1611.09347/) (2016)
- [Learning to Plan Chemical Syntheses](https://mlsanity.com/paper/1708.04202/) (2017)
- [Crystal Structure Prediction by Joint Equivariant Diffusion](https://mlsanity.com/paper/2309.04475/) (2023)
- [Periodic Graph Transformers for Crystal Material Property Prediction](https://mlsanity.com/paper/2209.11807/) (2022)
- [DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics](https://mlsanity.com/paper/1712.03641/) (2017)
- [Transition1x -- a Dataset for Building Generalizable Reactive Machine Learning Potentials](https://mlsanity.com/paper/2207.12858/) (2022)
- [Complete and Efficient Graph Transformers for Crystal Material Property Prediction](https://mlsanity.com/paper/2403.11857/) (2024)
- [Learning the exchange-correlation functional from nature with fully differentiable density functional theory](https://mlsanity.com/paper/2102.04229/) (2021)
- [Autonomous discovery of battery electrolytes with robotic experimentation and machine-learning](https://mlsanity.com/paper/2001.09938/) (2019)
- [NeuralNEB -- Neural Networks can find Reaction Paths Fast](https://mlsanity.com/paper/2207.09971/) (2022)
- [Physics-Driven Learning for Inverse Problems in Quantum Chromodynamics](https://mlsanity.com/paper/2501.05580/) (2025)
- [Machine Learning for Material Characterization with an Application for Predicting Mechanical Properties](https://mlsanity.com/paper/2010.06010/) (2020)
- [A Low-Cost Robot Science Kit for Education with Symbolic Regression for Hypothesis Discovery and Validation](https://mlsanity.com/paper/2204.04187/) (2022)
- [Interpolation and differentiation of alchemical degrees of freedom in machine learning interatomic potentials](https://mlsanity.com/paper/2404.10746/) (2024)

## Built on by (most cited first, 1 of 1)

- [GQD-AdsNet: Graph Neural Networks Unlock Rapid Exploration of Transition Metal Adsorption on Graphene Quantum Dots](https://mlsanity.com/paper/2607.18591/) (2026)

## Abstract (arXiv)

The rapid advancement of machine learning and artificial intelligence (AI)-driven techniques is revolutionizing materials discovery, property prediction, and material design by minimizing human intervention and accelerating scientific progress. This review provides a comprehensive overview of smart, machine learning (ML)-driven approaches, emphasizing their role in predicting material properties, discovering novel compounds, and optimizing material structures. Key methodologies in this field include deep learning, graph neural networks, Bayesian optimization, and automated generative models (GANs, VAEs). These approaches enable the autonomous design of materials with tailored functionalities. By leveraging AutoML frameworks (AutoGluon, TPOT, and H2O.ai), researchers can automate the model selection, hyperparameter tuning, and feature engineering, significantly improving the efficiency of materials informatics. Furthermore, the integration of AI-driven robotic laboratories and high-throughput computing has established a fully automated pipeline for rapid synthesis and experimental validation, drastically reducing the time and cost of material discovery. This review highlights real-world applications of automated ML-driven approaches in predicting mechanical, thermal, electrical, and optical properties of materials, demonstrating successful cases in superconductors, catalysts, photovoltaics, and energy storage systems. We also address key challenges, such as data quality, interpretability, and the integration of AutoML with quantum computing, which are essential for future advancements. Ultimately, combining AI with automated experimentation and computational modeling is transforming the way materials are discovered and optimized. This synergy paves the way for new innovations in energy, electronics, and nanotechnology.