Electron microscopy studies of soft nanomaterials
This review highlights recent efforts on applying electron microscopy (EM) to soft (including
biological) nanomaterials. We will show how developments of both the hardware and …
biological) nanomaterials. We will show how developments of both the hardware and …
[HTML][HTML] Scope of machine learning in materials research—A review
This comprehensive review investigates the multifaceted applications of machine learning in
materials research across six key dimensions, redefining the field's boundaries. It explains …
materials research across six key dimensions, redefining the field's boundaries. It explains …
Biomaterialomics: Data science-driven pathways to develop fourth-generation biomaterials
Conventional approaches to developing biomaterials and implants require intuitive tailoring
of manufacturing protocols and biocompatibility assessment. This leads to longer …
of manufacturing protocols and biocompatibility assessment. This leads to longer …
Synthesizing controlled microstructures of porous media using generative adversarial networks and reinforcement learning
For material modeling and discovery, synthetic microstructures play a critical role as digital
twins. They provide stochastic samples upon which direct numerical simulations can be …
twins. They provide stochastic samples upon which direct numerical simulations can be …
Rapid and flexible segmentation of electron microscopy data using few-shot machine learning
Automatic segmentation of key microstructural features in atomic-scale electron microscope
images is critical to improved understanding of structure–property relationships in many …
images is critical to improved understanding of structure–property relationships in many …
[HTML][HTML] Machine learning for materials design and discovery
R Vasudevan, G Pilania… - Journal of Applied Physics, 2021 - pubs.aip.org
We are excited to present this Special Topic collection on Machine Learning for Materials
Design and Discovery in the Journal of Applied Physics. With a wide range of exciting and …
Design and Discovery in the Journal of Applied Physics. With a wide range of exciting and …
Self-supervised learning and prediction of microstructure evolution with convolutional recurrent neural networks
Microstructural evolution is a key aspect of understanding and exploiting the processing-
structure-property relationship of materials. Modeling microstructure evolution usually relies …
structure-property relationship of materials. Modeling microstructure evolution usually relies …
Artificial intelligence in predicting mechanical properties of composite materials
The determination of mechanical properties plays a crucial role in utilizing composite
materials across multiple engineering disciplines. Recently, there has been substantial …
materials across multiple engineering disciplines. Recently, there has been substantial …
Deep learning-based discriminative refocusing of scanning electron microscopy images for materials science
Scanning electron microscopy (SEM) has contributed significantly to the development of
microstructural characteristics analysis in modern-day materials science. Although it is …
microstructural characteristics analysis in modern-day materials science. Although it is …
Performance and limitations of deep learning semantic segmentation of multiple defects in transmission electron micrographs
Transmission electron microscopy (TEM) is a popular method for characterizing and
quantifying defects in materials. Analyzing digitized TEM images is typically done manually …
quantifying defects in materials. Analyzing digitized TEM images is typically done manually …