124 citations · 173 across the 11 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…
Predicting Oxide Glass Properties with Low Complexity Neural Network and Physical and Chemical Descriptors
Suresh Bishnoi, Skyler Badge, Jayadeva +1
Due to their disordered structure, glasses present a unique challenge in predicting the composition-property relationships. Recently, several attempts have been made to predict the…
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…
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…
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…