83 citations · 433 across the 10 of their papers we have counts for
10 papers
GlassNet: a multitask deep neural network for predicting many glass properties
Daniel R. Cassar
A multitask deep neural network model was trained on more than 218k different glass compositions. This model, called GlassNet, can predict 85 different properties (such as optical,…
Glass Hardness: Predicting Composition and Load Effects via Symbolic Reasoning-Informed Machine Learning
Sajid Mannan, Mohd Zaki, Suresh Bishnoi +8
Glass hardness varies in a non-linear fashion with the chemical composition and applied load, a phenomenon known as the indentation size effect (ISE), which is challenging to predi…
Machine learning unveils composition-property relationships in chalcogenide glasses
Saulo M. Mastelini, Daniel R. Cassar, Edesio Alcobaça +3
Due to their unique optical and electronic functionalities, chalcogenide glasses are materials of choice for numerous microelectronic and photonic devices. However, to extend the r…
Is the Structural Relaxation of Glasses Controlled by Equilibrium Shear Viscosity?
Ricardo F. Lancelotti, Daniel R. Cassar, Marcelo Nalin +2
Knowledge of relaxation processes is fundamental in glass science and technology because relaxation is intrinsically related to vitrification, tempering as well as to annealing and…
Predicting and interpreting oxide glass properties by machine learning using large datasets
Daniel R. Cassar, Saulo Martiello Mastelini, Tiago Botari +3
With the advent of powerful computer simulation techniques, it is time to move from the widely used knowledge-guided empirical methods to approaches driven by data science, mainly…
Designing optical glasses by machine learning coupled with a genetic algorithm
Daniel R. Cassar, Gisele G. dos Santos, Edgar D. Zanotto
Engineering new glass compositions have experienced a sturdy tendency to move forward from (educated) trial-and-error to data- and simulation-driven strategies. In this work, we de…
ViscNet: Neural network for predicting the fragility index and the temperature-dependency of viscosity
Daniel R. Cassar
Viscosity () is one of the most important properties of disordered matter. The temperature-dependence of viscosity is used to adjust process variables for glass-making, from mel…
Which glass stability parameters can assess the glass-forming ability of oxide systems?
Jeanini Jiusti, Daniel R. Cassar, Edgar D. Zanotto
Glass forming ability (GFA) is a property of utmost importance in glass science and technology. In this paper, we used a statistical methodology---involving bootstrap sampling and…
Critical assessment of the alleged failure of the Classical Nucleation Theory at low temperatures
Daniel Roberto Cassar, André Hofmeister Serra, Oscar Peitl +1
The Classical Nucleation Theory allegedly fails to describe the temperature dependence of the homogeneous crystal nucleation rates below the temperature of maximum nucleation, $T_{…
Solving the Classical Nucleation Theory with respect to the surface energy
Daniel R. Cassar
An essential parameter of the Classical Nucleation Theory (CNT) is the surface energy between a critical-size nucleus and the ambient phase, . In condensed matter, this paramete…