Accurate crystal structure prediction (CSP) is essential for discovering novel materials. Although various CSP methods have been developed, systematic benchmarks and quantitative comparisons remain limited. In this study, we evaluate eight CSP approaches: the evolutionary algorithm USPEX, three versions of ab initio random sampling with symmetry constraints (USPEX, AIRSS, Pyxtal codes), generative machine learning models (generative adversarial neural network, GAN, and variational autoencoder, VAE), and two very different template-based structure generators (random topological structure generator of USPEX and CSPML). These tests are done through a case study on vanadium oxide systems V5O8 and V3O4. Our results show that most of the compared methods are capable of identifying low-energy and metastable structures when sufficient sampling is performed. However, each method exhibits distinct strengths and trade-offs in terms of accuracy, efficiency, structural diversity, and symmetry character. Notably, more established traditional methods, such as USPEX and AIRSS, offer robust performance across system sizes, while ML-based approaches demonstrate rapid structure generation with minimal sampling, albeit with a greater reliance on the quality of the training data and post-processing approaches. Interestingly, different versions of random sampling show very different performance. This study underscores the complementary nature of traditional and ML-based CSP strategies and provides practical guidance for selecting appropriate methods based on the complexity of the system, the computational resources available, and the specific discovery objectives.
An et al. (Thu,) studied this question.
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