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Found 5 papers, 4 papers with code
Evaluation of GlassNet for physics-informed machine learning of glass stability and glass-forming ability
Here, we explore the application of an open-source pre-trained NN model, GlassNet, that can predict the characteristic temperatures necessary to compute glass stability (GS) and assess the feasibility of using these physics-informed ML (PIML)-predicted GS parameters to estimate GFA.
Designing optical glasses by machine learning coupled with a genetic algorithm
In this work, we developed a computer program that combines data-driven predictive models (in this case, neural networks) with a genetic algorithm to design glass compositions with desired combinations of properties.
Materials Science Soft Condensed Matter Computational Physics
ViscNet: Neural network for predicting the fragility index and the temperature-dependency of viscosity
The final trained NN was tested with a test dataset of 85 liquids with different compositions than those used for training and validating the NN.
Computational Physics Disordered Systems and Neural Networks Soft Condensed Matter
Which glass stability parameters can assess the glass-forming ability of oxide systems?
Glass forming ability (GFA) is a property of utmost importance in glass science and technology.
Soft Condensed Matter
Solving the Classical Nucleation Theory with respect to the surface energy
A common practice to obtain $\sigma$ is to assume a model for its temperature-dependence and perform a regression of the CNT equation against experimental nucleation data.
Soft Condensed Matter Chemical Physics Computational Physics
