AlloyBERT: Alloy Property Prediction with Large Language Models
This addresses the challenge of alloy design for material scientists by offering a text-based predictive framework, though it is incremental as it adapts existing language models to a new domain.
The study tackled predicting alloy properties like elastic modulus and yield strength from textual inputs using AlloyBERT, a transformer-based model, achieving MSEs of 0.00015 and 0.00611 on two datasets, outperforming shallow models.
The pursuit of novel alloys tailored to specific requirements poses significant challenges for researchers in the field. This underscores the importance of developing predictive techniques for essential physical properties of alloys based on their chemical composition and processing parameters. This study introduces AlloyBERT, a transformer encoder-based model designed to predict properties such as elastic modulus and yield strength of alloys using textual inputs. Leveraging the pre-trained RoBERTa encoder model as its foundation, AlloyBERT employs self-attention mechanisms to establish meaningful relationships between words, enabling it to interpret human-readable input and predict target alloy properties. By combining a tokenizer trained on our textual data and a RoBERTa encoder pre-trained and fine-tuned for this specific task, we achieved a mean squared error (MSE) of 0.00015 on the Multi Principal Elemental Alloys (MPEA) data set and 0.00611 on the Refractory Alloy Yield Strength (RAYS) dataset. This surpasses the performance of shallow models, which achieved a best-case MSE of 0.00025 and 0.0076 on the MPEA and RAYS datasets respectively. Our results highlight the potential of language models in material science and establish a foundational framework for text-based prediction of alloy properties that does not rely on complex underlying representations, calculations, or simulations.