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

Abstract

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.

References in corpus (17)

• E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials
• A General-Purpose Machine Learning Framework for Predicting Properties of Inorganic Materials
• Machine Learning Unifies the Modelling of Materials and Molecules
• High-pressure phases of silane
• On-the-fly Closed-loop Autonomous Materials Discovery via Bayesian Active Learning
• Deep Potentials for Materials Science
• Autonomous discovery of battery electrolytes with robotic experimentation and machine-learning
• DPA-2: a large atomic model as a multi-task learner
• Learning the exchange-correlation functional from nature with fully differentiable density functional theory
• Machine learning for materials discovery: two-dimensional topological insulators
• Short-Range Order Structure Motifs Learned from an Atomistic Model of a Zr${}_{50}$Cu${}_{45}$Al${}_5$ Metallic Glass
• Periodic Graph Transformers for Crystal Material Property Prediction
• Toward Accurate Interpretable Predictions of Materials Properties within Transformer Language Models
• Training machine-learning potentials for crystal structure prediction using disordered structures
• Physics-Driven Learning for Inverse Problems in Quantum Chromodynamics
• Crystal Structure Prediction by Joint Equivariant Diffusion
• Complete and Efficient Graph Transformers for Crystal Material Property Prediction

Cited by in corpus (2)

• Materials Informatics: Emergence To Autonomous Discovery In The Age Of AI
• A critical assessment of bonding descriptors for predicting materials properties