Developing a Crystal Structure Prediction Algorithm with Agent-Guided Search

Language-model agents show promise for machine learning research, but evidence that agent-guided research can advance the state of the art on established tasks remains limited. We investigate this possibility in crystal structure prediction (CSP), a central task in materials discovery that combines affordable experimentation with a comparatively underexplored generative modeling landscape. An agent-guided search explores eighteen generative framework–architecture pairs and identifies masked generative modeling as the strongest approach under the initial training budget. Subsequent experimentation, combined with four targeted human interventions, produces MaskGXT, a masked generative crystal transformer that jointly predicts crystallographic symmetry, lattice parameters, and atomic coordinates. MaskGXT incorporates circular ordinal label smoothing, sub-bin coordinate refinement, and space-group-stratified sampling to address periodic geometry, coordinate precision, and polymorph coverage. Our experiments show that MaskGXT achieves the highest match rates compared to an extensive set of CSP baselines on MP-20 and MPTS-52 benchmarks.