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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

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

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
Development of a Transferable Inter-atomic Potential for Boroaluminosilicate Glasses
Effect of Polydispersity on the Fracture Properties of Calcium–Silicate–Hydrate Gel
Elucidating Compositional Governance of Optical Properties of Oxide Glasses Using Interpretable Machine Learning
Fusing Experimental and Simulation Datasets in Machine Learning for Predicting Glass Properties
Graph ODE for Learning Dynamic Systems
Impact of Irradiation on the Properties of Gel Layer Formed After Aqueous Corrosion of Borosilicate Glasses
Kinetic Monte Carlo Simulation of Glasses Aided by Machine Learning
Looking for Order in Disorder: Topological Data Analysis of Glass Structure
Machine Learning as a Tool to Accelerate the Design of Nuclear Waste Glasses with Enhanced Sulfur Loadings
Modeling Polaron Hopping in Ternary Spinel Oxides
Now On-Demand Only: Information Extraction Pipeline for Glasses: An NLP Based Approach
P1-3: Molecular Dynamic Characteristic Temperatures for Predicting Metallic Glass Forming Ability
The Energy Landscape Governs Ductility in Disordered Materials
Toward Revealing Full Atomic Picture of Nanoindentation Deformation Mechanisms in Li2O-2SiO2 Glass-ceramics

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