[HTML][HTML] Review of deep learning: concepts, CNN architectures, challenges, applications, future directions
In the last few years, the deep learning (DL) computing paradigm has been deemed the
Gold Standard in the machine learning (ML) community. Moreover, it has gradually become …
Gold Standard in the machine learning (ML) community. Moreover, it has gradually become …
Machine learning for high performance organic solar cells: current scenario and future prospects
A Mahmood, JL Wang - Energy & environmental science, 2021 - pubs.rsc.org
Machine learning (ML) is a field of computer science that uses algorithms and techniques for
automating solutions to complex problems that are hard to program using conventional …
automating solutions to complex problems that are hard to program using conventional …
Benzothiadiazole-based non-fullerene acceptors
The power conversion efficiencies (PCEs) of organic solar cells (OSCs) have been greatly
improved with the rapid development of non-fullerene acceptors (NFAs) in recent five years …
improved with the rapid development of non-fullerene acceptors (NFAs) in recent five years …
Deep learning models for cloud, edge, fog, and IoT computing paradigms: Survey, recent advances, and future directions
In recent times, the machine learning (ML) community has recognized the deep learning
(DL) computing model as the Gold Standard. DL has gradually become the most widely …
(DL) computing model as the Gold Standard. DL has gradually become the most widely …
Deep learning-based detection of aluminum casting defects and their types
Due to its unique properties, high-pressure aluminum die-casting parts are used quite often,
especially in the automotive industry. However, die-casting is a process which requires non …
especially in the automotive industry. However, die-casting is a process which requires non …
Machine learning boosts the design and discovery of nanomaterials
Y Jia, X Hou, Z Wang, X Hu - ACS Sustainable Chemistry & …, 2021 - ACS Publications
Nanomaterials (NMs) have developed quickly and cover various fields, but research on
nanotechnology and NMs largely relies on costly experiments or complex calculations (eg …
nanotechnology and NMs largely relies on costly experiments or complex calculations (eg …
[HTML][HTML] A generalizable and interpretable deep learning model to improve the prediction accuracy of strain fields in grid composites
Recently, the design of grid composites with superior mechanical properties has gained
significant attention as a testbed for deep neural network (DNN)-based optimization …
significant attention as a testbed for deep neural network (DNN)-based optimization …
Building a quantitative composition-microstructure-property relationship of dual-phase steels via multimodal data mining
The establishment of composition-microstructure-property relationship is a long-standing
topic in materials science, yet neither continuum mechanics approaches nor machine …
topic in materials science, yet neither continuum mechanics approaches nor machine …
Machine learning–assisted design of material properties
Designing functional materials requires a deep search through multidimensional spaces for
system parameters that yield desirable material properties. For cases where conventional …
system parameters that yield desirable material properties. For cases where conventional …
Deep learning for variable renewable energy: a systematic review
J Klaiber, C Van Dinther - ACM Computing Surveys, 2023 - dl.acm.org
In recent years, both fields, AI and VRE, have received increasing attention in scientific
research. Thus, this article's purpose is to investigate the potential of DL-based applications …
research. Thus, this article's purpose is to investigate the potential of DL-based applications …