182 citations · 1.1k across the 28 of their papers we have counts for
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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…
Revealing the Atomic Structure of Silicate Glasses by Force-Enhanced Atomic Refinement
Qi Zhou, Tao Du, Lijie Guo +2
Although experiments can offer some fingerprints of the atomic structure of glasses (coordination numbers, pair distribution function, etc.), atomistic simulations are often requir…
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…
Machine Learning Forcefield for Silicate Glasses
Han Liu, Zipeng Fu, Yipeng Li +2
Developing accurate, transferable, and computationally-efficient interatomic forcefields is key to facilitate the modeling of silicate glasses. However, the high number of forcefie…
Prediction of Silicate Glasses' Stiffness by High-Throughput Molecular Dynamics Simulations and Machine Learning
Kai Yang, Xinyi Xu, Benjamin Yang +3
The development by machine learning of models predicting materials' properties usually requires the use of a large number of consistent data for training. However, quality experime…