Elucidating the constitutive relationship of calcium-silicate-hydrate gel using high throughput reactive molecular simulations and machine learning.

ABSTRACT: Prediction of material behavior using machine learning (ML) requires consistent, accurate, and, representative large data for training. However, such consistent and reliable experimental datasets are not always available for materials. To address this challenge, we synergistically integrate ML with high-throughput reactive molecular dynamics (MD) simulations to elucidate the constitutive relationship of calcium-silicate-hydrate (C-S-H) gel-the primary binding phase in concrete formed via the hydration of ordinary portland cement. Specifically, a highly consistent dataset on the nine elastic constants of more than 300 compositions of C-S-H gel is developed using high-throughput reactive simulations. From a comparative analysis of various ML algorithms including neural networks (NN) and Gaus

SUBMITTER: Lyngdoh GA 

PROVIDER: S-EPMC7721899 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature