Results 211 to 220 of about 2,817,119 (330)
AI is transforming the research paradigm of battery materials and reshaping the entire landscape of battery technology. This comprehensive review summarizes the cutting‐edge applications of AI in the advancement of battery materials, underscores the critical challenges faced in harnessing the full potential of AI, and proposes strategic guidance for ...
Qingyun Hu +5 more
wiley +1 more source
Rician Likelihood Loss for Quantitative MRI With Self-Supervised Deep Learning. [PDF]
Parker CS +5 more
europepmc +1 more source
A novel descriptor and a bottom‐up design principle are established to enable the rational design of hydrogen storage materials based on d‐block transition metal single‐atom COFs. By modulating H₂ adsorption through d‐orbital tuning, this approach achieves both high storage capacity and fast kinetics, while revealing a volcano‐type relationship between
Qiuyan Yue +24 more
wiley +1 more source
Deep-Learning Driven Identification of Novel Antimicrobial Peptides. [PDF]
Arino S +8 more
europepmc +1 more source
A 3D bone scaffold with osteogenic properties and capable of hardening in vivo is developed. The scaffold is implanted in a ductile state, and a phase transformation of the ceramic induces the stiffening and strengthening of the scaffold in vivo. Abstract Calcium phosphate 3D printing has revolutionized customized bone grafting.
Miguel Mateu‐Sanz +7 more
wiley +1 more source
DeepInMiniscope: Deep learning-powered physics-informed integrated miniscope. [PDF]
Tian F, Mattison B, Yang W.
europepmc +1 more source
Recycling of Thermoplastics with Machine Learning: A Review
This review shows how machine learning is revolutionizing mechanical, chemical, and biological pathways, overcoming traditional challenges and optimizing sorting, efficiency, and quality. It provides a detailed analysis of effective feature engineering strategies and establishes a forward‐looking research agenda for a truly circular thermoplastic ...
Rodrigo Q. Albuquerque +5 more
wiley +1 more source
Deep learning for electroencephalography emotion recognition. [PDF]
Pourrostami H +4 more
europepmc +1 more source

