Results 241 to 250 of about 2,363,364 (342)
Phonons‐informed machine‐learning predictive models are propitious for reproducing thermal effects in computational materials science studies. Machine learning (ML) methods have become powerful tools for predicting material properties with near first‐principles accuracy and vastly reduced computational cost.
Pol Benítez +4 more
wiley +1 more source
Schumann-anchored golden ratio organization of human neural oscillations. [PDF]
Lacy M.
europepmc +1 more source
Materials informatics and autonomous experimentation are transforming the discovery of organic molecular crystals. This review presents an integrated molecule–crystal–function–optimization workflow combining machine learning, crystal structure prediction, and Bayesian optimization with robotic platforms.
Takuya Taniguchi +2 more
wiley +1 more source
Analysis and research on iron loss of multi-layer interior permanent magnet synchronous motor for electric vehicle applications. [PDF]
Niu L.
europepmc +1 more source
A Critical Assessment of Bonding Descriptors for Predicting Materials Properties
The impact of new bonding descriptors in machine learning models for predicting material properties is assessed. Improvements are validated using significance tests, and new, intuitive descriptors for screening lattice thermal conductivity and projected force constants are introduced.
Aakash Ashok Naik +6 more
wiley +1 more source
High-resolution Dataset of Electric Vehicle Charging Responses Under Varied Power Quality Disturbances. [PDF]
Li H, Zhang Y, Yang S, Liu X.
europepmc +1 more source
Multi-source harmonic estimation method for distribution networks based on variational modal decomposition. [PDF]
Zuo H, Xu H, Wang Z, Yu Z, Wu Z.
europepmc +1 more source
Novel Technology for Unbalance Diagnosis for Dual-Speed Wind Turbines. [PDF]
Askari AR +4 more
europepmc +1 more source
Power System Harmonics: The Effect on Power Quality
openaire +1 more source

