Learning k-Resolved Electronic Structure via Soft Energy Occupancy Prediction

Dongik Park, Kunmin Jang, Jaewon Bae, Dongin Kim, Chanyoung Park

Keywords: k-Resolved Electronic Structure, Electronic Structure Prediction, Graph Neural Networks, High-Throughput Materials Screening

Abstract: Predicting electronic structure from crystal geometry is essential for computational materials discovery, as it determines key physical quantities such as band gaps, DOS, and energy isosurfaces. While per-band prediction has been explored, it requires fixing the number of bands or indexing each band across k-points, limiting generality across materials. Predicting k-resolved electronic structure avoids these constraints; we propose kRESForge, which predicts energy bin occupancy at each k-point. Given a crystal structure and a query k-point, the model predicts a probability distribution over 256 energy bins spanning $\pm10$ eV from the Fermi level, providing native uncertainty estimates. Band structure visualization follows directly from k-path queries, and downstream physical quantities such as band gaps, DOS, and energy isosurfaces can be derived through k-space aggregation without additional training. On 28,517 non-magnetic materials from the Materials Project, kRESForge achieves a band gap MAE of 0.39 eV, 90\% metal/non-metal classification accuracy, and DOS MAE of 2.64 states/eV.

Submission Track: Paper Track (Tiny Paper)

Submission Category: AI-Guided Design

Submission Number: 38