82 citations · 263 across the 9 of their papers we have counts for
9 papers
From Patents to Dataset: Scraping for Oxide Glass Compositions and Properties
Gustavo Laranja Thomaello, Thomaz Yeiden Busnardo Aguena, Eric Trevelato Costa +4
In this work, we present web scraping techniques to extract in- formation from patent tables, clean and structure them for future use in predictive machine learning models to devel…
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…
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_{…
Rebuttal to Schmelzer and Tropin: Glass Transition, Crystallization of Glass-Forming Melts, and Entropy
Edgar D. Zanotto, John C. Mauro
In a recent article, Schmelzer and Tropin [Entropy 20 [2] 103 (2018)] presented an unfounded, confusing critique of several aspects of modern glass science. Relying on pre-Socratic…