# Crystalline Material Discovery in the Era of Artificial Intelligence

arXiv: https://arxiv.org/abs/2408.08044 · HTML: https://mlsanity.com/paper/2408.08044/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Aug 2024.

## Built on by (most cited first, 11 of 11)

- [Materials Generation in the Era of Artificial Intelligence: A Comprehensive Survey](https://mlsanity.com/paper/2505.16379/) (2025)
- [Establishing baselines for generative discovery of inorganic crystals](https://mlsanity.com/paper/2501.02144/) (2025)
- [A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools](https://mlsanity.com/paper/2506.20743/) (2025)
- [Generative Models for Crystalline Materials](https://mlsanity.com/paper/2511.22652/) (2025)
- [Active Learning for Conditional Inverse Design with Crystal Generation and Foundation Atomic Models](https://mlsanity.com/paper/2502.16984/) (2025)
- [VisionLaw: Inferring Interpretable Intrinsic Dynamics from Visual Observations via Bilevel Optimization](https://mlsanity.com/paper/2508.13792/) (2025)
- [Efficient Equivariant High-Order Crystal Tensor Prediction via Cartesian Local-Environment Many-Body Coupling](https://mlsanity.com/paper/2602.04323/) (2026)
- [Design Topological Materials by Reinforcement Fine-Tuned Generative Model](https://mlsanity.com/paper/2504.13048/) (2025)
- [Role of Large Language Models and Retrieval-Augmented Generation for Accelerating Crystalline Material Discovery: A Systematic Review](https://mlsanity.com/paper/2508.06691/) (2025)
- [Generative Inverse Design of Cold Metals for Low-Power Electronics](https://mlsanity.com/paper/2603.13920/) (2026)
- [Adopting Reinforcement Learning with Verifiable Rewards for Molecular Generation](https://mlsanity.com/paper/2607.19044/) (2026)

## Abstract (arXiv)

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.