124 citations · 143 across the 6 of their papers we have counts for
6 papers
Revealing the Compositional Control of Electrical, Mechanical, Optical, and Physical Properties of Inorganic Glasses
R. Ravinder, Suresh Bishnoi, Mohd Zaki +1
Inorganic glasses, produced by the melt-quenching of a concoction of minerals, compounds, and elements, can possess unique optical and elastic properties along with excellent chemi…
Unveiling the Glass Veil: Elucidating the Optical Properties in Glasses with Interpretable Machine Learning
Mohd Zaki, Vineeth Venugopal, R. Ravinder +5
Due to their excellent optical properties, glasses are used for various applications ranging from smartphone screens to telescopes. Developing compositions with tailored Abbe numbe…
Scalable Gaussian Processes for Predicting the Properties of Inorganic Glasses with Large Datasets
Suresh Bishnoi, R. Ravinder, Hargun Singh +2
Gaussian process regression (GPR) is a useful technique to predict composition--property relationships in glasses as the method inherently provides the standard deviation of the pr…
Deep Learning Aided Rational Design of Oxide Glasses
R. Ravinder, Karthikeya H. Sreedhara, Suresh Bishnoi +5
Despite the extensive usage of oxide glasses for a few millennia, the composition-property relationships in these materials still remain poorly understood. While empirical and phys…
Cooling Rate Effects on the Structure of 45S5 Bioglass: Computational and Experimental Evidence of Si--P Avoidance
Pratik Bhaskar, Yashasvi Maurya, Rajesh Kumar +10
Due to its ability to bond with living tissues upon dissolution, 45S5 bioglass and related compositions are promising materials for the replacement, regeneration, and repair of har…
Predicting Young's Modulus of Glasses with Sparse Datasets using Machine Learning
Suresh Bishnoi, Sourabh Singh, R. Ravinder +4
Machine learning (ML) methods are becoming popular tools for the prediction and design of novel materials. In particular, neural network (NN) is a promising ML method, which can be…