Atomistic Language Models Understand and Generate Materials

Atomistic structure and natural language have long been modeled separately, with language models either calling atomistic models as tools or being fine-tuned on lossy textual encodings that discard atomistic information. We introduce **Atomistic Language Models (ALMs)** to pursue native multimodality, in which a single language backbone understands atomistic structures, generates materials from natural language, and optimizes crystal structures as instructed by text. By unifying a pretrained atomistic encoder, large language model, and denoising diffusion model through purely continuous projectors and staged training, ALMs achieve state-of-the-art results on crystal structure prediction and *de novo* generation. ALMs are enabled by a *continuous bridge that maps language model embeddings directly into the steering space of atomistic diffusion*, and are assisted by **Text-to-Crystal Feynman–Kac (T2C-FK)**, a particle-based sampler that scores partial denoising trajectories to enforce stoichiometric targets at inference time. We also introduce **ALM Bench**, the first benchmark for language-instructed crystal generation and materials optimization. ALMs beat frontier LLMs, including GPT-5.6 Sol, on inorganic crystalline doping, inverse design, and generation for desired applications. We release all code, training data, and model weights.