Powder X-ray diffraction (XRD) patterns are highly effective for crystal identification and play a pivotal role in materials discovery. While machine learning (ML) has advanced the analysis of powder XRD patterns, progress has been constrained by the limited availability of training data and established benchmarks. To address this, we introduce SimXRD, the largest open-source simulated XRD pattern dataset to date, aimed at accelerating the development of crystallographic informatics.

We developed a novel XRD simulation method that incorporates comprehensive physical interactions, resulting in a high-fidelity database. SimXRD comprises 4,065,346 simulated powder XRD patterns, representing 119,569 unique crystal structures under 33 simulated conditions that reflect real-world variations. We benchmark 21 sequence models in both in-library and out-of-library scenarios and analyze the impact of class imbalance in long-tailed crystal label distributions.

Materials discovery is a fundamental driver of technological advancement with direct impact on real-world challenges. From energy systems and electronics to biomedical devices and sustainable manufacturing, novel materials enable new functionalities and improved performance.

Our survey offers a detailed and comprehensive overview of material representations, particularly focusing on crystal structures and their precise mathematical definitions. We systematically categorize and compare a wide range of existing techniques, supported by a clear development timeline. To facilitate research and practical application, we provide abundant resources, including links to open-source code and datasets. Additionally, we highlight current challenges and propose future research directions to inspire continued innovation in the field.

A notable difficulty in applying machine learning to this domain is the lack of sufficiently sized experimental datasets, which has constrained researchers to train primarily on simulated data. However, models trained on simulated pXRD patterns showed limited generalization to experimental patterns, particularly for low-quality experimental patterns with high noise levels and elevated backgrounds.

With the Open Experimental Powder X-Ray Diffraction Database (opXRD), we provide an openly available and easily accessible dataset of labeled and unlabeled experimental powder diffractograms. Labeled opXRD data can be used to evaluate the performance of models on experimental data and unlabeled opXRD data can help improve the performance of models on experimental data, e.g. through transfer learning methods. We collected 92552 diffractograms, 2179 of them labeled, from a wide spectrum of materials classes.

The active learning approach dynamically balances exploration and exploitation.

All active learning algorithms in Bgolearn framework are open-sourced, supporting materials informatics development.

In this work, we present a crystal generative framework based on Wyckoff generative adversarial network (CGWGAN) to efficiently discover novel crystals.

The CGWGAN includes three modules: a generator of crystal templates, an atom-infill module, and a crystal screening module. The generator uses a generative adversarial network (GAN) to produce crystal templates embedded with asymmetry units (ASUs), space groups, lattice vectors, and the total number of atoms within the lattice cell, ensuring that the generated templates precisely match all requirements of crystals. These templates become crystal candidates after filling in atoms of different chemical elements. These candidates are screened by M3GNet and the passed ones are subjected to density functional theory (DFT)-based calculations to finally verify their stability. As a showcase, the CGWGAN successfully discovers seven novel crystals within the Ba-Ru-O system, demonstrating its effectiveness. This work provides a knowledge-guided Artificial Intelligence generative framework for accelerating crystal discovery.