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Laurent K. Beland

Publications and source records attributed to Laurent K. Beland.

2 recordsLinked to original sources

Interlayer Pores Play a Limited Role in Diffusion Through Hydrated Na-MMT: Insights from a Multiscale, Experimentally Anchored Model

This study investigates interlayer diffusion dynamics in sodium montmorillonite (Na--MMT), a smectite clay widely used in environmental remediation, pharmaceutical formulations, and advanced materials. Understanding diffusion in Na--MMT is critical, yet current models often rely on fitted parameters rather than directly linking transport to microscopic structure; even when the structure is known, interlayer diffusion remains challenging to model. This motivates the development of a predictive, coarse-grained, geometry-based computational framework. Our multiscale framework couples atomistic simulations with a coarse-grained mesoscale model to quantify contributions from interlayer one-, two-, and three-water pores, as well as free pores ($>3$-water diameter), across dry densities of $0.8$--$1.3~\mathrm{g\,cm^{-3}}$. Experimentally derived platelet size distributions, polydispersity, and anisotropic transport behavior are explicitly incorporated. Results indicate that interlayer pores contribute minimally to overall water diffusion at the studied densities, with transport dominated by free pores. Predicted diffusion scaling factors closely match tritium tracer measurements when interlayer throttling is included, and the model captures the pronounced anisotropy of Na--MMT. Validation against lattice Boltzmann simulations and experiments demonstrates reliable reproduction of geometric tortuosity and pore-size distributions. Despite limitations, including rigid platelets and omission of three-water energy minima, the coarse-grained framework provides a robust platform for understanding nanoconfined diffusion. Future work will focus on refining interlayer energy landscapes and incorporating flexible platelet mechanics.

cond-mat.mtrl-sci↗

Deep Learning and Crystal Plasticity: A Preconditioning Approach for Accurate Orientation Evolution Prediction

Efficient and precise prediction of plasticity by data-driven models relies on appropriate data preparation and a well-designed model. Here we introduce an unsupervised machine learning-based data preparation method to maximize the trainability of crystal orientation evolution data during deformation. For Taylor model crystal plasticity data, the preconditioning procedure improves the test score of an artificial neural network from 0.831 to 0.999, while decreasing the training iterations by an order of magnitude. The efficacy of the approach was further improved with a recurrent neural network. Electron backscattered (EBSD) lab measurements of crystal rotation during rolling were compared with the results of the surrogate model, and despite error introduced by Taylor model simplifying assumptions, very reasonable agreement between the surrogate model and experiment was observed. Our method is foundational for further data-driven studies, enabling the efficient and precise prediction of texture evolution from experimental and simulated crystal plasticity results.

cond-mat.mtrl-sci↗