Projects per year
Abstract
Machine learning (ML) is emerging as a powerful tool to predict the properties of materials, including glasses. Informing ML models with knowledge of how glass composition affects short-range atomic structure has the potential to enhance the ability of composition-property models to extrapolate accurately outside of their training sets. Here, we introduce an approach wherein statistical mechanics informs a ML model that can predict the non-linear composition-structure relations in oxide glasses. This combined model offers an improved prediction compared to models relying solely on statistical physics or machine learning individually. Specifically, we show that the combined model accurately both interpolates and extrapolates the structure of Na2O–SiO2 glasses. Importantly, the model is able to extrapolate predictions outside its training set, which is evidenced by the fact that it is able to predict the structure of a glass series that was kept fully hidden from the model during its training.
Original language | English |
---|---|
Article number | 192 |
Journal | npj Computational Materials |
Volume | 8 |
Issue number | 1 |
Number of pages | 9 |
ISSN | 2057-3960 |
DOIs | |
Publication status | Published - 9 Sept 2022 |
Fingerprint
Dive into the research topics of 'Predicting glass structure by physics-informed machine learning'. Together they form a unique fingerprint.Projects
- 1 Finished
-
Tailoring the Structure of Disordered Solids using Statistical Mechanics
Smedskjær, M. M. (PI) & Bødker, M. S. (Project Participant)
01/09/2017 → 31/08/2021
Project: Research
Press/Media
-
Predicting glass structure by physics-informed machine learning
Du, T., Smedskjær, M. M. & Bødker, M. S.
09/09/2022
1 item of Media coverage
Press/Media: Press / Media