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 - Composition represntation (The composition of a material in Hill notation ).

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-composition-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

To be published

BibTeX:

[More Information Needed]

APA: