Machine learning for glass science and engineering: A review
The design of new glasses is often plagued by poorly efficient Edisonian “trial-and-error”
discovery approaches. As an alternative route, the Materials Genome Initiative has largely …
discovery approaches. As an alternative route, the Materials Genome Initiative has largely …
Model-driven design of bioactive glasses: from molecular dynamics through machine learning
M Montazerian, ED Zanotto… - International Materials …, 2020 - journals.sagepub.com
Research in bioactive glasses (BGs) has traditionally been performed through trial-and-error
experimentation. However, several modelling techniques will accelerate the discovery of …
experimentation. However, several modelling techniques will accelerate the discovery of …
Predicting the Young's modulus of silicate glasses using high-throughput molecular dynamics simulations and machine learning
K Yang, X Xu, B Yang, B Cook, H Ramos… - Scientific reports, 2019 - nature.com
The application of machine learning to predict materials' properties usually requires a large
number of consistent data for training. However, experimental datasets of high quality are …
number of consistent data for training. However, experimental datasets of high quality are …
Predicting densities and elastic moduli of SiO2-based glasses by machine learning
Chemical design of SiO2-based glasses with high elastic moduli and low weight is of great
interest. However, it is difficult to find a universal expression to predict the elastic moduli …
interest. However, it is difficult to find a universal expression to predict the elastic moduli …
Designing optical glasses by machine learning coupled with a genetic algorithm
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 …
Predicting the dissolution kinetics of silicate glasses by topology-informed machine learning
H Liu, T Zhang, NM Anoop Krishnan… - Npj Materials …, 2019 - nature.com
Abstract Machine learning (ML) regression methods are promising tools to develop models
predicting the properties of materials by learning from existing databases. However …
predicting the properties of materials by learning from existing databases. However …
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 …
Porous but mechanically robust all-inorganic antireflective coatings synthesized using polymers of intrinsic microporosity
Here, we introduce polymer of intrinsic microporosity 1 (PIM-1) to design single-layer and
multilayered all-inorganic antireflective coatings (ARCs) with excellent mechanical …
multilayered all-inorganic antireflective coatings (ARCs) with excellent mechanical …
Machine learning enabled models to predict sulfur solubility in nuclear waste glasses
The US Department of Energy is considering implementing the direct feed approach for the
vitrification of low-activity waste (LAW) and high-level waste (HLW) at the Hanford site in …
vitrification of low-activity waste (LAW) and high-level waste (HLW) at the Hanford site in …
Artificial intelligence and machine learning in glass science and technology: 21 challenges for the 21st century
Glasses have been an integral part of human life for more than 2000 years. Despite several
years of research and analysis, some fundamental and practical questions on glasses still …
years of research and analysis, some fundamental and practical questions on glasses still …