Overview: Computer vision and machine learning for microstructural characterization and analysis
Microstructural characterization and analysis is the foundation of microstructural science,
connecting materials structure to composition, process history, and properties …
connecting materials structure to composition, process history, and properties …
Progress report on phase separation in polymer solutions
Polymeric porous media (PPM) are widely used as advanced materials, such as sound
dampening foams, lithium‐ion batteries, stretchable sensors, and biofilters. The functionality …
dampening foams, lithium‐ion batteries, stretchable sensors, and biofilters. The functionality …
Material structure-property linkages using three-dimensional convolutional neural networks
The core materials knowledge needed in the accelerated design, development, and
deployment of new and improved materials is most accessible when cast in the form of …
deployment of new and improved materials is most accessible when cast in the form of …
Synergetic effect of N/O functional groups and microstructures of activated carbon on supercapacitor performance by machine learning
Heteroatoms-rich activated carbon (AC) can effectively promote the pseudo-capacitance of
AC-based electrodes used in supercapacitors. The well-known microstructural properties of …
AC-based electrodes used in supercapacitors. The well-known microstructural properties of …
Structure–property linkages using a data science approach: application to a non-metallic inclusion/steel composite system
Practical multiscale materials design is contingent on the availability of robust and reliable
reduced-order linkages (ie, surrogate models) between the material internal structure and its …
reduced-order linkages (ie, surrogate models) between the material internal structure and its …
Reduced-order structure-property linkages for polycrystalline microstructures based on 2-point statistics
Computationally efficient structure-property (SP) linkages (ie, reduced order models) are a
necessary key ingredient in accelerating the rate of development and deployment of …
necessary key ingredient in accelerating the rate of development and deployment of …
Materials informatics
S Ramakrishna, TY Zhang, WC Lu, Q Qian… - Journal of Intelligent …, 2019 - Springer
Materials informatics employs techniques, tools, and theories drawn from the emerging
fields of data science, internet, computer science and engineering, and digital technologies …
fields of data science, internet, computer science and engineering, and digital technologies …
[HTML][HTML] Prediction of two-phase composite microstructure properties through deep learning of reduced dimensional structure-response data
GA Sengodan - Composites Part B: Engineering, 2021 - Elsevier
A novel method to predict the mechanical responses of arbitrary microstructures from the
deep learning of microstructures and their stress-strain response is presented in this work …
deep learning of microstructures and their stress-strain response is presented in this work …
Process-structure linkages using a data science approach: application to simulated additive manufacturing data
A novel data science workflow is developed and demonstrated to extract process-structure
linkages (ie, reduced-order model) for microstructure evolution problems when the final …
linkages (ie, reduced-order model) for microstructure evolution problems when the final …
[HTML][HTML] Inverse methods for design of soft materials
Functional soft materials, comprising colloidal and molecular building blocks that self-
organize into complex structures as a result of their tunable interactions, enable a wide array …
organize into complex structures as a result of their tunable interactions, enable a wide array …