Matlantis CSP(MTCSP) is a crystal structure prediction service offered on the cloud-based atomic-level AI simulation platform Matlantis. Upon receiving user-specified conditions, the service generates candidate crystal structures (atomic configurations), evaluates them based on energy metrics, and systematically explores more stable (or potentially promising) structures. The challenge of crystal structure prediction stems from two key factors: first, the vast search space involved, and second, the significant computational time required for structure evaluation. The effectiveness of different search approaches varies dramatically depending on the elemental composition and experimental conditions. This means that structure exploration isn’t simply a matter of increasing candidate generation – within limited computational resources, careful consideration must be given to: * how to efficiently generate candidate structures * how to effectively evaluate structures * how to configure the search loop MTCSP was developed to enable practical execution of extensive searches by treating both the search execution and result analysis as service components. This allows users to focus exclusively on specifying purpose-appropriate conditions and conducting analysis tailored to their specific applications. While MTCSP provides numerous features for efficiently conducting crystal structure predictions and easily analyzing results, its primary involvement with Optuna lies in the search process. Specifically, it plays a critical role in both structural generation algorithms (how to create candidate crystal structures for evaluation) and the configuration of the search loop. MTCSP operates by alternating between phases for structural generation/search and phases for structural relaxation/energy evaluation, ultimately performing phase diagram evaluation and outputting new crystal structures. The search loop implementation in MTCSP combines Optuna with custom functionalities as needed, rather than simply utilizing Optuna’s core features. The search is controlled by an “Experiment” class, which serves as a wrapper around Optuna’s “Study” class responsible for managing the search process. This class provides various convenient features for MTCSP users, including: – add_pure_atoms: Adds single-atom crystals corresponding to specified elemental systems – create_initial_population: Generates initial populations for MTCSP-specific genetic algorithms Recently, we launched the crystal structure prediction service Matlantis CSP (MTCSP) on our cloud-based atomic-level AI simulation platform Matlantis™︎. For MTCSP, we have implemented the black-box optimization framework Optuna™︎—which PFN leads in developing—as an approach to facilitate efficient and effective crystal structure prediction through minimal trial iterations.
