82 citations · 263 across the 9 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…