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cond-mat.mtrl-sci2026
Probing Materials Knowledge in LLMs: From Latent Embeddings to Reliable Predictions
Vineeth Venugopal, Soroush Mahjoubi, Elsa Olivetti
Large language models are increasingly applied to materials science, yet fundamental questions remain about their reliability and knowledge encoding. Evaluating 25 LLMs across four…
cond-mat.mtrl-sci2025
DiffSyn: A Generative Diffusion Approach to Materials Synthesis Planning
Elton Pan, Soonhyoung Kwon, Sulin Liu +9
The synthesis of crystalline materials, such as zeolites, remains a significant challenge due to a high-dimensional synthesis space, intricate structure-synthesis relationships and…
cond-mat.mtrl-sci2025
Language Models Enable Data-Augmented Synthesis Planning for Inorganic Materials
Thorben Prein, Elton Pan, Janik Jehkul +3
Inorganic synthesis planning currently relies primarily on heuristic approaches or machine-learning models trained on limited datasets, which constrains its generality. We demonstr…