Recent advances and applications of deep learning methods in materials science
Deep learning (DL) is one of the fastest-growing topics in materials data science, with
rapidly emerging applications spanning atomistic, image-based, spectral, and textual data …
rapidly emerging applications spanning atomistic, image-based, spectral, and textual data …
Data‐Driven Materials Innovation and Applications
Owing to the rapid developments to improve the accuracy and efficiency of both
experimental and computational investigative methodologies, the massive amounts of data …
experimental and computational investigative methodologies, the massive amounts of data …
Machine learning for catalysis informatics: recent applications and prospects
T Toyao, Z Maeno, S Takakusagi, T Kamachi… - Acs …, 2019 - ACS Publications
The discovery and development of catalysts and catalytic processes are essential
components to maintaining an ecological balance in the future. Recent revolutions made in …
components to maintaining an ecological balance in the future. Recent revolutions made in …
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 …
Statistical characterization of the morphologies of nanoparticles through machine learning based electron microscopy image analysis
Although transmission electron microscopy (TEM) may be one of the most efficient
techniques available for studying the morphological characteristics of nanoparticles …
techniques available for studying the morphological characteristics of nanoparticles …
Understanding important features of deep learning models for segmentation of high-resolution transmission electron microscopy images
Cutting edge deep learning techniques allow for image segmentation with great speed and
accuracy. However, application to problems in materials science is often difficult since these …
accuracy. However, application to problems in materials science is often difficult since these …
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 …
Toward excellence of electrocatalyst design by emerging descriptor‐oriented machine learning
Abstract Machine learning (ML) is emerging as a powerful tool for identifying quantitative
structure–activity relationships to accelerate electrocatalyst design by learning from historic …
structure–activity relationships to accelerate electrocatalyst design by learning from historic …
Deep learning analysis on microscopic imaging in materials science
Microscopic imaging providing the real-space information of matter, plays an important role
for understanding the correlations between structure and properties in the field of materials …
for understanding the correlations between structure and properties in the field of materials …
Machine learning in electron microscopy for advanced nanocharacterization: current developments, available tools and future outlook
In the last few years, electron microscopy has experienced a new methodological paradigm
aimed to fix the bottlenecks and overcome the challenges of its analytical workflow. Machine …
aimed to fix the bottlenecks and overcome the challenges of its analytical workflow. Machine …