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cond-mat.mtrl-sci2026
Foundation-Model Surrogates Enable Data-Efficient Active Learning for Materials Discovery
Jeffrey Hu, Rongzhi Dong, Ying Feng +2
Active learning (AL) has emerged as a powerful paradigm for accelerating materials discovery by iteratively steering experiments toward promising candidates, reducing the number of…
cond-mat.mtrl-sci2025★ 1 cited
In context learning Foundation models for Materials Property Prediction with Small datasets
Qinyang Li, Rongzhi Dong, Nicholas Miklaucic +6
Foundation models (FMs) have recently shown remarkable in-context learning (ICL) capabilities across diverse scientific domains. In this work, we introduce a unified in-context lea…
cond-mat.mtrl-sci2024
Improving realistic material property prediction using domain adaptation based machine learning
Jeffrey Hu, David Liu, Nihang Fu +1
Materials property prediction models are usually evaluated using random splitting of datasets into training and test datasets, which not only leads to over-estimated performance du…