A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques
Artificial intelligence (AI) is one of the emerging technologies. In recent decades, artificial
intelligence (AI) has gained widespread acceptance in a variety of fields, including virtual …
intelligence (AI) has gained widespread acceptance in a variety of fields, including virtual …
Artificial intelligence-enabled smart mechanical metamaterials: advent and future trends
Mechanical metamaterials have opened an exciting venue for control and manipulation of
architected structures in recent years. Research in the area of mechanical metamaterials …
architected structures in recent years. Research in the area of mechanical metamaterials …
Benchmarking machine learning models for polymer informatics: an example of glass transition temperature
In the field of polymer informatics, utilizing machine learning (ML) techniques to evaluate the
glass transition temperature T g and other properties of polymers has attracted extensive …
glass transition temperature T g and other properties of polymers has attracted extensive …
Machine learning-based glass formation prediction in multicomponent alloys
Metallic glasses (MGs) have attracted considerable academic attention owing to their unique
properties and great application prospects. Unlike other glassy materials, such as oxide …
properties and great application prospects. Unlike other glassy materials, such as oxide …
Polymer graph neural networks for multitask property learning
O Queen, GA McCarver, S Thatigotla… - npj Computational …, 2023 - nature.com
The prediction of a variety of polymer properties from their monomer composition has been a
challenge for material informatics, and their development can lead to a more effective …
challenge for material informatics, and their development can lead to a more effective …
A review on Machine learning aspect in physics and mechanics of glasses
The glass science and technology is a rapidly developing field which is focused on
development of new glasses with excellent properties. Glasses are the non-crystalline …
development of new glasses with excellent properties. Glasses are the non-crystalline …
Designing optical glasses by machine learning coupled with a genetic algorithm
DR Cassar, GG Santos, ED Zanotto - Ceramics international, 2021 - Elsevier
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 …
from (educated) trial-and-error to data-and simulation-driven strategies. In this work, we …
GlassNet: a multitask deep neural network for predicting many glass properties
DR Cassar - Ceramics International, 2023 - Elsevier
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 …
compositions. This model, called GlassNet, can predict 85 different properties (such as …
Predicting the effective atomic number of glass systems using machine learning algorithms
This study investigates the calculation of the effective atomic number (Z eff) of glass systems
through the application of machine learning algorithms. Specifically, Artificial Neural …
through the application of machine learning algorithms. Specifically, Artificial Neural …
ViscNet: Neural network for predicting the fragility index and the temperature-dependency of viscosity
DR Cassar - Acta materialia, 2021 - Elsevier
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 melting to …
dependence of viscosity is used to adjust process variables for glass-making, from melting to …