ASTRA: Updated LUMO Predictor (Final)
This model is part of the ASTRA (Advanced Solvation Transformer for Rational Additives) framework. It is an updated BERT-based regression model designed to predict the Binding Energy (Eb) of additive molecules from SMILES.
Unlike the initial predictor, this "Final" version has been re-trained and updated using the datasets generated during the ASTRA Active Learning loop. Consequently, it exhibits improved predictive accuracy and robustness, particularly within the targeted chemical space of high-reward electrolyte additives.
Model Details
- Architecture: BERT (Sequence Classification / Regression)
- Stage: Final (After Active Learning Loop)
- Task: Property Prediction (Eb)
- Base Model: Updated from
wkdghdus23/astra-predictor-initial-lumo
Usage
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 = ["Eb"]
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.