metadata
license: apache-2.0
tags:
- materials
- multimodal-projector
ALM · structure-to-language projector
The projector that maps frozen OrbV3 machine-learning-interatomic-potential
features into the Qwen3-8B token space: a small MLP (Linear(256→4096) → GELU → Linear(4096→4096), ~21M params) whose outputs are spliced into the input sequence
as soft tokens at the <atoms> position. The encoder produces one feature
vector per atom; the projector emits one soft token per atom. Frozen in the
generation models; trained in ALM Core.
Inputs: OrbV3 (orb_v3_direct_20_omat) 256-d per-atom features → 4096-d soft tokens.
Links
Paper: arXiv · HuggingFace · Code: GitHub
License
Apache-2.0.
Citation
@article{edamadaka2026atomistic,
title = {Atomistic Language Models Understand and Generate Materials},
author = {Edamadaka, Sathya and Ramesh, Krithik and Li, Ju and G\'omez-Bombarelli, Rafael},
journal = {arXiv preprint arXiv:2606.21395},
year = {2026}
}