Professor Daniel R. Cassar

@article{Cassar_2023,

title = {GlassNet: A multitask deep neural network for predicting many glass properties},

author = {Daniel R. Cassar},

url = {https://doi.org/10.1016%2Fj.ceramint.2023.08.281},

doi = {10.1016/j.ceramint.2023.08.281},

year  = {2023},

date = {2023-08-01},

urldate = {2023-08-01},

journal = {Ceramics International},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{WELCH2023118994,

title = {Cracking the Kauzmann paradox},

author = {Rebecca S. Welch and Edgar D. Zanotto and Collin J. Wilkinson and Daniel R. Cassar and Maziar Montazerian and John C. Mauro},

url = {https://www.sciencedirect.com/science/article/pii/S1359645423003257},

doi = {https://doi.org/10.1016/j.actamat.2023.118994},

issn = {1359-6454},

year  = {2023},

date = {2023-01-01},

journal = {Acta Materialia},

volume = {254},

pages = {118994},

abstract = {The Kauzmann paradox and associated Kauzmann temperature are two of the most widely debated topics in glass science over the past eighty years. Both conceptualized by Walter Kauzmann in 1948, the Kauzmann paradox occurs when some supercooled liquids apparently exhibit a negative excess entropy at temperatures above absolute zero. The Kauzmann temperature is the temperature at which the excess entropy vanishes. This review provides a retrospective on the origin of these hypotheses and their study through energy landscapes, crystallization behavior, and viscosity models. We also provide a critical analysis of each approach. After nearly eighty years of research, there is no conclusive evidence that supports the concepts proposed by Kauzmann. As such, it can be concluded that future work should be focused elsewhere.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{MANNAN2023119046,

title = {Glass hardness: Predicting composition and load effects via symbolic reasoning-informed machine learning},

author = {Sajid Mannan and Mohd Zaki and Suresh Bishnoi and Daniel R. Cassar and Jeanini Jiusti and Julio Cesar Ferreira Faria and Johan F. S. Christensen and Nitya Nand Gosvami and Morten M. Smedskjaer and Edgar Dutra Zanotto and N. M. Anoop Krishnan},

url = {https://www.sciencedirect.com/science/article/pii/S1359645423003774},

doi = {https://doi.org/10.1016/j.actamat.2023.119046},

issn = {1359-6454},

year  = {2023},

date = {2023-01-01},

journal = {Acta Materialia},

volume = {255},

pages = {119046},

abstract = {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 predict quantitatively. Here, using a curated dataset of over 3,000 inorganic glasses from the literature comprising the composition, indentation load, and hardness, we develop machine learning (ML) models to predict the composition and load dependence of Vickers hardness. Interestingly, when tested on new glass compositions unseen during the training, the standard data-driven ML model failed to capture the ISE. To address this gap, we combined an empirical expression (Bernhardt's equation) to describe the ISE with ML to develop a framework that incorporates the symbolic equation representing the domain reasoning in ML, namely Symbolic Reasoning-Informed ML Procedure (SRIMP). We show that the resulting SRIMP outperforms the data-driven ML model in predicting the ISE. Finally, we interpret the SRIMP model to understand the contribution of the glass network formers and modifiers toward composition and load-dependent (ISE) and load-independent hardness. The deconvolution of the hardness into load-dependent and load-independent terms paves the way toward a holistic understanding of the composition effect and ISE in glasses, enabling efficient and accelerated discovery of new glass compositions with targeted hardness.},

keywords = {Glass composition, Hardness, Indentation load, Indentation Size Effect (ISE), Machine Learning (ML)},

pubstate = {published},

tppubtype = {article}

}