This model is part of the ASTRA (Advanced Solvation Transformer for Rational Additives) framework. It is a BERT-based regression model fine-tuned from the pre-trained astra-bert-mlm. This specific model is designed to predict the Lowest Unoccupied Molecular Orbital (LUMO) levels of given additive molecules on their solvation structure based on SMILES.

This is the "Initial" version of the predictor, meaning it was trained on the baseline dataset before the ASTRA Active Learning loop began.

Model Details

Architecture: BERT (Sequence Classification / Regression)
Stage: Initial (Before Active Learning)
Task: Property Prediction (LUMO)
Base Model: wkdghdus23/astra-bert-mlm

Usage

python

from astra.tokenizer import initial_bert_tokenizer_with_vocabulary
from astra.model import BertForDownstream, EmbeddingTunedModel

# Load the tokenizer and model
vocab_file = "./vocab.txt"

target_name = ["LUMO"]

tokenizer = initial_bert_tokenizer_with_vocabulary(path=vocab_file)
model = EmbeddingTunedModel.from_pretrained_embtune_model(pretrained_path=pretrained_model_path,
		tokenizer=tokenizer,
		target_name=target_name)

More Information

For more details on data preparation, downstream fine-tuning, and the full active learning loop, please visit our GitHub Repository.

wkdghdus23/astra-predictor-initial-lumo

ASTRA: Initial LUMO Predictor

Model Details

Usage

More Information

wkdghdus23/
astra-predictor-initial-lumo