Natural Language Embeddings of Synthesis and Testing conditions Enhance Glass Dissolution Prediction

TL;DR

This study demonstrates that incorporating natural language embeddings of synthesis and testing conditions into machine learning models significantly improves the prediction of glass dissolution rates, aiding nuclear waste management.

Contribution

The paper introduces a novel approach using NLP embeddings combined with structural descriptors to enhance glass dissolution prediction and enable extrapolation to new compositions.

Findings

NLP-ML model outperforms classical ML in predicting dissolution rates.

The integrated model generalizes to glass compositions with unseen elements.

Transforming compositional features to structural descriptors improves extrapolation.

Abstract

Long-term chemical durability of glass, crucial for immobilizing nuclear waste, is governed by glass properties such as composition, surface geometry, as well as external factors like thermodynamic conditions and surrounding medium. Despite decades of research, there are no models that account for these intrinsic and extrinsic factors to predict the dissolution rates of glass compositions. To address this challenge, we evaluate the role of natural language embeddings capturing the synthesis and testing conditions in enhancing the predictability of glass dissolution. Evaluating the approach on hand-curated ~700 datapoints extracted from the literature, we reveal that the machine learning (ML) model including natural language embeddings (NLP-ML) outperforms classical ML model in predicting glass dissolution rate. Furthermore, we developed a generalizable ML model by transforming the compositional features to structural descriptors of glass alongside NLP-derived features, enabling extrapolation capability to glass compositions with completely new elements absent in the training data. Evaluating this model on a completely new dataset of glass compositions 34 chemical components in contrast to the training dataset that had only 28 components, we demonstrate that the model indeed exhibits generalizability to glass compositions that are out-of-distribution. Altogether, this integrated approach offers a pathway towards high-fidelity glass dissolution prediction and accelerate the discovery of novel glass compositions with tailored durability for sustainable nuclear waste management.