Large-Scale Statistical Learning for Mass Transport Prediction in Porous Materials Using 90,000 Artificially Generated Microstructures
Artikel i vetenskaplig tidskrift, 2021

Effective properties of functional materials crucially depend on their 3D microstructure. In this paper, we investigate quantitative relationships between descriptors of two-phase microstructures, consisting of solid and pores and their mass transport properties. To that end, we generate a vast database comprising 90,000 microstructures drawn from nine different stochastic models, and compute their effective diffusivity and permeability as well as various microstructural descriptors. To the best of our knowledge, this is the largest and most diverse dataset created for studying the influence of 3D microstructure on mass transport. In particular, we establish microstructure-property relationships using analytical prediction formulas, artificial (fully-connected) neural networks, and convolutional neural networks. Again, to the best of our knowledge, this is the first time that these three statistical learning approaches are quantitatively compared on the same dataset. The diversity of the dataset increases the generality of the determined relationships, and its size is vital for robust training of convolutional neural networks. We make the 3D microstructures, their structural descriptors and effective properties, as well as the code used to study the relationships between them available open access.

virtual materials testing

porous materials


structure-property relationship

mass transport

deep learning



Benedikt Prifling

Universität Ulm

Magnus Röding

Chalmers, Matematiska vetenskaper, Tillämpad matematik och statistik

RISE Research Institutes of Sweden

Philip Townsend

RISE Research Institutes of Sweden

Chalmers, Matematiska vetenskaper, Tillämpad matematik och statistik

Matthias Neumann

Universität Ulm

Volker Schmidt

Universität Ulm

Frontiers in Materials

22968016 (eISSN)

Vol. 8 786502


Bioinformatik (beräkningsbiologi)

Metallurgi och metalliska material




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