7 papers
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…
PackFlow: Generative Molecular Crystal Structure Prediction via Reinforcement Learning Alignment
Akshay Subramanian, Elton Pan, Juno Nam +6
Organic molecular crystals underpin technologies ranging from pharmaceuticals to organic electronics, yet predicting solid-state packing of molecules remains challenging because ca…
Masked Mineral Modeling: Continent-Scale Mineral Prospecting via Geospatial Infilling
Sujay Nair, Evan Coleman, Sherrie Wang +1
Minerals play a critical role in the advanced energy technologies necessary for decarbonization, but characterizing mineral deposits hidden underground remains costly and challengi…
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…
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…
Contrastive Learning of English Language and Crystal Graphs for Multimodal Representation of Materials Knowledge
Yang Jeong Park, Mayank Kumaran, Chia-Wei Hsu +2
Artificial intelligence (AI) is increasingly used for the inverse design of materials, such as crystals and molecules. Existing AI research on molecules has integrated chemical str…