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Meeting MS&T21: Materials Science & Technology
Symposium Ceramics and Glasses Modeling by Simulations and Machine Learning
Presentation Title Decomposing the Strength of Hydrated Cement Compositions by Machine Learning
Author(s) Yu Song, Gaurav Sant, Mathieu Bauchy
On-Site Speaker (Planned) Yu Song
Abstract Scope Reducing the carbon footprint in cement production is a pressing challenge faced by the construction industry. To curb the massive environmental impact, it is pertinent to improve material performance and reduce the carbon embodiment of cement. This requires an in-depth understanding of how the strength of hydrated cement is controlled by the chemical composition of cement. Although this problem has been investigated for more than one hundred years, our current knowledge is still deficient for a clear decomposition of this complex composition-strength relationship. Here, we approach this problem using Gaussian process regression (GPR). Among all machine learning methods applied to the same dataset, our GPR model achieves the highest accuracy of predicting the strength of hydrated cement composite based on the chemical compounds. Based on the optimized GPR model, we are able to decompose the influence of each oxide on strength to an unprecedented level.
Proceedings Inclusion? Undecided


A Machine-learning Based Hierarchical Framework to Discover Novel Scintillator Chemistries
Bayesian Optimization of Silicon Nitride Empirical Potentials
Ceramics from Polymers –– Results of Ab Initio Molecular Dynamic Simulations
Deciphering the Viscosity of Glass Materials with Machine Learning
Decomposing the Strength of Hydrated Cement Compositions by Machine Learning
Development of a Reactive Force Field (ReaxFF) for Simulation of Polymer-derived Ceramics
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Now On-Demand Only: Information Extraction Pipeline for Glasses: An NLP Based Approach
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