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 …
Deep materials informatics: Applications of deep learning in materials science
A Agrawal, A Choudhary - Mrs Communications, 2019 - cambridge.org
The growing application of data-driven analytics in materials science has led to the rise of
materials informatics. Within the arena of data analytics, deep learning has emerged as a …
materials informatics. Within the arena of data analytics, deep learning has emerged as a …
Deep learning in electron microscopy
JM Ede - Machine Learning: Science and Technology, 2021 - iopscience.iop.org
Deep learning is transforming most areas of science and technology, including electron
microscopy. This review paper offers a practical perspective aimed at developers with …
microscopy. This review paper offers a practical perspective aimed at developers with …
BaGuaLu: targeting brain scale pretrained models with over 37 million cores
Large-scale pretrained AI models have shown state-of-the-art accuracy in a series of
important applications. As the size of pretrained AI models grows dramatically each year in …
important applications. As the size of pretrained AI models grows dramatically each year in …
Scalable reinforcement-learning-based neural architecture search for cancer deep learning research
Cancer is a complex disease, the understanding and treatment of which are being aided
through increases in the volume of collected data and in the scale of deployed computing …
through increases in the volume of collected data and in the scale of deployed computing …
HPC AI500: a benchmark suite for HPC AI systems
In recent years, with the trend of applying deep learning (DL) in high performance scientific
computing, the unique characteristics of emerging DL workloads in HPC raise great …
computing, the unique characteristics of emerging DL workloads in HPC raise great …
Exhaustive study of hierarchical allreduce patterns for large messages between GPUs
Data-parallel distributed deep learning requires an AllReduce operation between all GPUs
with message sizes in the order of hundreds of megabytes. The popular implementation of …
with message sizes in the order of hundreds of megabytes. The popular implementation of …
Resilience and robustness of spiking neural networks for neuromorphic systems
Though robustness and resilience are commonly quoted as features of neuromorphic
computing systems, the expected performance of neuromorphic systems in the face of …
computing systems, the expected performance of neuromorphic systems in the face of …
Theory-training deep neural networks for an alloy solidification benchmark problem
MT Rad, A Viardin, GJ Schmitz, M Apel - Computational Materials Science, 2020 - Elsevier
Deep neural networks are machine learning tools that are transforming fields ranging from
speech recognition to computational medicine. In this study, we extend their application to …
speech recognition to computational medicine. In this study, we extend their application to …