Foundation-Model Surrogates Enable Data-Efficient Active Learning for Materials Discovery
Active learning (AL) has emerged as a powerful paradigm for accelerating materials discovery by iteratively steering experiments toward promising candidates, reducing the number of costly synthesis-and-characterization cycles needed to identify optimal materials. However, current AL relies predominantly on Gaussian Process (GP) and Random Forest (RF) surrogates, which suffer from complementary limitations: GP underfits complex composition-property landscapes due to rigid kernel assumptions, while RF produces unreliable heuristic uncertainty estimates in small-data regimes. This small-data challenge is pervasive in materials science, making reliable surrogate modeling extremely difficult w...
| Authors | Jeffrey Hu, Rongzhi Dong, Ying Feng et al. |
|---|---|
| Primary category | cond-mat.mtrl-sci |
| Published | 13 March 2026 |
| Last updated at source | 24 March 2026 |
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