Crystal Structure Prediction of Inorganic Materials: A Benchmark and Modern Evaluation

Crystal structure prediction (CSP) of inorganic materials is a fundamental challenge in computational materials science, yet the field has historically lacked well‐defined benchmarks and comprehensive evaluations. We address this gap by introducing CSP180, a standardized benchmark of 180 inorganic materials, and evaluate 13 state‐of‐the‐art CSP algorithms spanning template‐based, machine learning (ML) potential‐based, and deep learning‐based approaches, none relying on density functional theory (DFT). As a baseline, we also assess the leading DFT‐based algorithms, CALYPSO and USPEX, on a 23‐structure subset with simpler compositions. Template‐based methods, such as TCSP and CSPML, achieve the highest space group match rates (58.9% and 47.2%) when a suitable template exists, while de novo algorithms struggle to identify correct space groups, with the best performers reaching only around 17%. On the 23‐structure subset, ML potential‐based algorithms prove highly competitive with DFT‐based methods at identifying lower‐energy structures, and this performance depends strongly on both potential quality and search algorithm efficiency. This assessment provides a standardized framework, quantitative metrics, and open‐source tools to guide future CSP development and accelerate materials discovery. Code and benchmark data are available at https://github.com/usccolumbia/cspbenchmark .