162 citations · 306 across the 9 of their papers we have counts for
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Natural Language Embeddings of Synthesis and Testing conditions Enhance Glass Dissolution Prediction
Sajid Mannan, K. Sidharth Nambudiripad, Indrajeet Mandal +2
Long-term chemical durability of glass, crucial for immobilizing nuclear waste, is governed by glass properties such as composition, surface geometry, as well as external factors l…
UniFFBench: Evaluating Universal Machine Learning Force Fields Against Experimental Measurements
Sajid Mannan, Vaibhav Bihani, Carmelo Gonzales +5
Universal machine learning force fields (UMLFFs) promise to revolutionize materials science by enabling rapid atomistic simulations across the periodic table. However, their evalua…
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…
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…
Quantized Friction across Ionic Liquid Thin Films
Alexander M. Smith, Kevin R. J. Lovelock, Nitya Nand Gosvami +2
Ionic liquids, salts in the liquid state under ambient conditions, are of great interest as precision lubricants. Ionic liquids form layered structures at surfaces, yet it is not c…