2 papers
cs.LG2026
Rethinking Molecular Text Representations for LLMs: An Empirical Study
Arun Raja, Garrett M. Morris, Kian Ming A. Chai
Large language models (LLMs) are increasingly used for molecular tasks, but it remains unclear which molecular representation to use. We present a systematic benchmark evaluating L…
cs.LG2026
Learning Inter-Atomic Potentials without Explicit Equivariance
Ahmed A. Elhag, Arun Raja, Alex Morehead +6
Accurate and scalable machine-learned inter-atomic potentials (MLIPs) are essential for molecular simulations ranging from drug discovery to new material design. Current state-of-t…