What question did this study set out to answer?

The aim is to optimize the stability and performance of underwater cement-based materials using a combined approach of molecular dynamics and machine learning.

March 21, 2026Open Access

Optimized design method for underwater anti-dispersive cement-based materials based on multi-scale coupling of molecular dynamics and machine learning

Puntos clave

The aim is to optimize the stability and performance of underwater cement-based materials using a combined approach of molecular dynamics and machine learning.
Developed a multi-scale framework incorporating molecular dynamics simulations and machine learning techniques.
Extracted key microstructural parameters using molecular dynamics, including interaction energies and diffusion coefficients.
Trained an XGBoost model on experimental data to predict fluidity, turbidity, and strength of the materials.
Achieved high R² values (0.939, 0.941, 0.979) for fluidity, turbidity, and strength, indicating strong predictive accuracy.
Improved predictions over traditional models—by 13.1% for fluidity, 13.6% for turbidity, and 48.1% for strength.
Identified interfacial interaction energy and molecular diffusion as dominant factors affecting the properties of the cement material.

Resumen

Water flow in underwater grouting causes severe slurry dispersion, compromising cement-based material stability. Traditional empirical mix design cannot simultaneously optimize fluidity, anti-dispersion performance, and mechanical strength. To solve this, we propose a multi-scale framework combining molecular dynamics (MD) simulation and machine learning (ML) to link microstructure to macroscopic properties in a Polyacrylamide-Graphene Oxide-U-type Expansive Agent (PAM-GO-UEA) -modified ultrafine cement system. From MD, we extract interfacial interaction energy, radial distribution functions, and ion/molecule diffusion coefficients to quantify interfacial bonding, structural density, and hydration kinetics in the Calcium Silicate Hydrate(C-S-H)–polymer–mineral system. Using experimental fluidity, turbidity, and compressive strength data, we train an XGBoost–SHAP model to identify key microstructural drivers. Five-fold cross-validation gives R 2 values of 0.939, 0.941, and 0.979 for fluidity, turbidity, and strength—improving on response surface modeling by 13.1%, 13.6%, and 48.1%. SHAP analysis shows E int dominates fluidity (0.14) and strength (0.165), while D (N) most strongly influences turbidity (0.17). • Multi-scale MD–ML framework for the design of underwater anti-dispersion grouts is developed. • MD descriptors ( E int , RDF, MSD, D ) provide quantitative microstructural indicators. • XGBoost–SHAP achieves high-accuracy prediction from small MD–experiment datasets. • SHAP reveals dominant microstructural factors governing flowability and strength.

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