Model Card for Model ID
Model Pretrained using Masked Language Modelling on 2 million crystal structures in one of the MatText Representation
Model Details
Model Description
MatText model pretrained using Masked Language Modelling on crystal structures mined from NOMAD and represented using MatText - CIF Symmetrized represntation (The CIF representation of a material in higher symmetry). .
- Developed by: Lamalab
- Homepage: https://github.com/lamalab-org/MatText
- Leaderboard: To be published
- Point of Contact: Nawaf Alampara
- Model type: Pretrained BERT
- Language(s) (NLP): This is not a natural language model
- License: MIT
Model Sources
- Repository: https://github.com/lamalab-org/MatText
- Paper: To be published
Uses
Direct Use
The base model can be used for generating meaningful features/embeddings of bulk structures without further training. This model is ideal if finetuned for narrowdown tasks.
Downstream Use
This model can be used with fientuning for property prediction, classification or extractions.
Bias, Risks, and Limitations
Model was trained only on bulk structures (n0w0f/MatText - pretrain2m - dataset).
The pertaining dataset is a subset of the materials deposited in the NOMAD archive. We queried only 3D-connected structures (i.e., excluding 2D materials, which often require special treatment) and, for consistency, limited our query to materials for which the bandgap has been computed using the PBE functional and the VASP code.
Recommendations
How to Get Started with the Model
from transformers import AutoModel
model = AutoModel.from_pretrained("n0w0f/MatText-cifsymmetrized-2m")
Training Details
Training Data
n0w0f/MatText - pretrain2m The dataset contains crystal structures in various text representations and labels for some subsets.
https://huggingface.co/datasets/n0w0f/MatText
Training Procedure
Training Hyperparameters
- Training regime: fp32
Testing Data, Factors & Metrics
Testing Data
https://huggingface.co/datasets/n0w0f/MatText/viewer/pretrain2m/test
Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type: 8 A100 GPUs with 40GB
- Hours used: 72h
- Cloud Provider: Private Infrastructure
- Compute Region: US/EU
- Carbon Emitted: 250W x 72h = 18 kWh x 0.432 kg eq. CO2/kWh = 7.78 kg eq. CO2
Technical Specifications
Software
Pretrained using https://github.com/lamalab-org/MatText
Citation
If you use MatText in your work, please cite
@misc{alampara2024mattextlanguagemodelsneed,
title={MatText: Do Language Models Need More than Text & Scale for Materials Modeling?},
author={Nawaf Alampara and Santiago Miret and Kevin Maik Jablonka},
year={2024},
eprint={2406.17295},
archivePrefix={arXiv},
primaryClass={cond-mat.mtrl-sci}
url={https://arxiv.org/abs/2406.17295},
}
Model Card Authors
The model was trained by Nawaf Alampara (n0w0f), Santiago Miret (LinkedIn), and Kevin Maik Jablonka (kjappelbaum).