Explainable machine learning in materials science
Abstract Machine learning models are increasingly used in materials studies because of
their exceptional accuracy. However, the most accurate machine learning models are …
their exceptional accuracy. However, the most accurate machine learning models are …
Computational microstructure characterization and reconstruction: Review of the state-of-the-art techniques
Building sensible processing-structure-property (PSP) links to gain fundamental insights and
understanding of materials behavior has been the focus of many works in computational …
understanding of materials behavior has been the focus of many works in computational …
Carbon–cement supercapacitors as a scalable bulk energy storage solution
The large-scale implementation of renewable energy systems necessitates the development
of energy storage solutions to effectively manage imbalances between energy supply and …
of energy storage solutions to effectively manage imbalances between energy supply and …
Characterization and analysis of porosity and pore structures
LM Anovitz, DR Cole - Reviews in Mineralogy and …, 2015 - pubs.geoscienceworld.org
Porosity plays a clearly important role in geology. It controls fluid storage in aquifers, oil and
gas fields and geothermal systems, and the extent and connectivity of the pore structure …
gas fields and geothermal systems, and the extent and connectivity of the pore structure …
Accelerating phase-field-based microstructure evolution predictions via surrogate models trained by machine learning methods
D Montes de Oca Zapiain, JA Stewart… - npj Computational …, 2021 - nature.com
The phase-field method is a powerful and versatile computational approach for modeling the
evolution of microstructures and associated properties for a wide variety of physical …
evolution of microstructures and associated properties for a wide variety of physical …
Guiding the design of heterogeneous electrode microstructures for Li‐ion batteries: microscopic imaging, predictive modeling, and machine learning
Electrochemical and mechanical properties of lithium‐ion battery materials are heavily
dependent on their 3D microstructure characteristics. A quantitative understanding of the …
dependent on their 3D microstructure characteristics. A quantitative understanding of the …
Microstructural materials design via deep adversarial learning methodology
Identifying the key microstructure representations is crucial for computational materials
design (CMD). However, existing microstructure characterization and reconstruction (MCR) …
design (CMD). However, existing microstructure characterization and reconstruction (MCR) …
An improved 3D microstructure reconstruction approach for porous media
Microstructure reconstruction of porous media is vital for the evaluation of material
properties, which has been applied in many fields. Various approaches have been …
properties, which has been applied in many fields. Various approaches have been …
[图书][B] Stochastic geometry and its applications
SN Chiu, D Stoyan, WS Kendall, J Mecke - 2013 - books.google.com
An extensive update to a classic text Stochastic geometry and spatial statistics play a
fundamental role in many modern branches of physics, materials sciences, engineering …
fundamental role in many modern branches of physics, materials sciences, engineering …
GPU-based discrete element model of realistic non-convex aggregates: Mesoscopic insights into ITZ volume fraction and diffusivity of concrete
Aggregates with realistic non-convex morphologies and their surrounding interfacial
transition zones (ITZs) significantly affect the diffusivity of concrete. Herein, we initially …
transition zones (ITZs) significantly affect the diffusivity of concrete. Herein, we initially …