Artificial intelligence in materials science and engineering: Current landscape, key challenges, and future trajectories
🤖 AI Summary
Materials development faces significant challenges including data complexity, lengthy timelines, and low efficiency, necessitating intelligent approaches to accelerate discovery. This work provides a systematic review of artificial intelligence applications in materials science, integrating a spectrum of techniques from traditional machine learning to deep learning and generative AI. It focuses on representation methods and model construction for multimodal data—such as composition, structure, images, and text—and covers core algorithms including convolutional neural networks (CNNs), graph neural networks (GNNs), Transformers, and Gaussian processes. The review particularly highlights emerging directions such as uncertainty quantification, multi-source data fusion, and language-inspired representations, proposing a research pathway toward intelligent materials design. By offering a comprehensive AI framework, this study identifies critical challenges in data quality, standardization, and algorithmic adaptability, thereby significantly enhancing the efficiency and reliability of materials discovery and optimization.
Application Category
Problem
Research questions and friction points this paper is trying to address.
Artificial Intelligence
Materials Science
Data Quality
Data Standardization
Machine Learning
Innovation
Methods, ideas, or system contributions that make the work stand out.
machine learning
deep learning
generative AI
materials representation
uncertainty quantification
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