Crystalline Material Discovery in the Era of Artificial Intelligence

2024-08-15

arXiv.org

AI Summary

Current AI-driven crystal materials discovery faces persistent bottlenecks: poor generalizability, limited interpretability, and absence of experimental closed-loop validation. To address these challenges, this work establishes the first unified analytical framework that systematically reviews AI advances across four core tasks—property prediction, synthesis design, characterization assistance, and theoretical computation acceleration. It clarifies the alignment mechanisms between crystal representations (e.g., crystal graphs, electron density grids, atomic sequences) and deep learning architectures (e.g., graph neural networks, 3D convolutional networks, sequence models, multi-task learning). A comprehensive taxonomy is proposed, categorizing methods by model, dataset, and task, thereby delineating their scope and limitations. Finally, the paper identifies two critical frontiers for breakthrough: scalable representation learning and physics-informed modeling. This study provides a foundational methodology and technical roadmap for next-generation intelligent materials platforms. (149 words)

Technology Category

• Machine Learning: Deep Neural Architectures and Foundation Models
• Search and Optimization: Metareasoning and Metaheuristics
• Humans and AI: Other Foundations of Human Computation & AI

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Crystalline materials, with symmetrical and periodic structures, exhibit a wide spectrum of properties and have been widely used in numerous applications across electronics, energy, and beyond. For crystalline materials discovery, traditional experimental and computational approaches are time-consuming and expensive. In these years, thanks to the explosive amount of crystalline materials data, great interest has been given to data-driven materials discovery. Particularly, recent advancements have exploited the expressive representation ability of deep learning to model the highly complex atomic systems within crystalline materials, opening up new avenues for fast and accurate materials discovery. These works typically focus on four types of tasks, including physicochemical property prediction, crystalline material synthesis, aiding characterization, and accelerating theoretical computations. Despite the remarkable progress, there is still a lack of systematic investigation to summarize their distinctions and limitations. To fill this gap, we systematically investigated the progress made in recent years. We first introduce several data representations of the crystalline materials. Based on the representations, we summarize various fundamental deep learning models and their tailored usages in various material discovery tasks. Finally, we highlight the remaining challenges and propose future directions.

Problem

• Deep Learning
• Material Discovery
• Technological Challenges

Innovation

• Deep Learning
• Crystal Materials
• Predictive Modeling