Surrogate-Gated Generation and Foundation-Model Embeddings for Bayesian Materials Design
Closed-loop materials discovery iterates between proposing candidate structures and evaluating their properties, and property evaluation dominates the cost. In the generative variant, a learned prior proposes candidate crystals and a property oracle scores them; we ask whether a cheap probabilistic surrogate can triage the generator's output, and what such a surrogate must do well. Across three architecturally distinct pretrained diffusion priors (MatterGen, CrystalFlow, ADiT) and two targets (room-temperature heat capacity and bulk modulus), we insert a Gaussian process acquisition gate between structure generation and the oracle in an RL-steered generative workflow. The gate matches or ...
| Authors | Sk Md Ahnaf Akif Alvi, Jan Janssen, Danny Perez et al. |
|---|---|
| Primary category | cond-mat.mtrl-sci |
| Published | 26 June 2026 |
| Last updated at source | 26 June 2026 |
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