Results 231 to 240 of about 10,905,813 (301)

Engineering of Crystal and Domain Structures in Epitaxial Y:HfO2 Thin Films by YSZ Substrate Miscut

Advanced Science, EarlyView.
We investigate how YSZ substrate miscut influences crystal structure and domain formation in epitaxial Y‐doped HfO2 thin films. Using magnetron sputtering, high‐resolution X‐ray diffraction, atomic‐resolution scanning transmission electron microscopy, and first‐principles calculations, we systematically examine the characteristics of thickness‐ and ...
Jun Young Lee, Hyung‐Jin Choi, Kun Hee Ye, Haneul Choi, Byeong‐hyeon Lee, Min‐Seok Kim, Dong‐Hun Han, June Hyuk Lee, Sung Ok Won, Tae Heon Kim, Hye Jung Chang, Jung‐Hae Choi, Seung‐Hyub Baek   +12 more
wiley   +1 more source

Inferring Branch-Specific Rates of Lineage Diversification Under the Birth-Death-Shift Process. [PDF]

Syst Biol
Höhna S, Freyman WA, Nolen Z, Huelsenbeck JP, May MR, Rannala B, Moore BR.   +6 more
europepmc   +1 more source

Broadening Hard‐Magnet Discovery Beyond Symmetry Constraints via Unified Effective Anisotropy

Advanced Science, EarlyView.
A unified effective‐anisotropy descriptor (Keff) extends hard‐magnet screening across all seven crystal systems, beyond the uniaxial restriction of conventional searches. Machine‐learning screening of 9320 known ferromagnets and diffusion‐model generation together yield 38 rare‐earth‐free or ‐lean candidates with DFT‐validated magnetic hardness (κ > 1),
Hojae Kim, Hyeondeok Shin, Kyungju Nam, Seunghyo Noh, Donghwi Kim, Yousung Jung   +5 more
wiley   +1 more source

From redundant association rules to product networks: A scalable confidence-improvement pruning and Maximum Spanning Tree approach. [PDF]

PLoS One
Đurišić V, Vujošević S, Kašćelan L, Vuković S.   +3 more
europepmc   +1 more source

High‐Throughput Screening and Interpretable Machine Learning for Rational Design of Bimetallic Catalysts for Methane Activation

Advanced Science, EarlyView.
ABSTRACT Methane's efficient catalytic removal is vital for sustainable development. Bimetallic catalysts, though promising for methane activation, pose a design challenge due to their complex compositional space. This work introduces an integrated framework that combines high‐throughput density functional theory (DFT) and interpretable machine ...
Mingzhang Pan, Tian Zhang, Jiawei Dong, Yubao Xie, Kaifeng Zhong, Wei Guan, Haiqiao Wei, Changcheng Fu, Yaqiong Su   +8 more
wiley   +1 more source

Smartwatch and physical education exam stress: the role of exercise self-efficacy and physical self-concept. [PDF]

Front Psychiatry
Liu Q, Yang H, Cui Q, Chen J, Shi B.
europepmc   +1 more source

Functional Disorder at the Neural Interface: How Disordered Nanostructures Promote Proper Growth and Differentiation in In Vitro Neural Cultures

Advanced Science, EarlyView.
This work provides a practical guide for neuroengineers to design advanced neural interfaces, embracing and tailoring the concept of functional disorder. By bridging 2D and 3D in vitro models, this work highlights how non‐periodic, spatially heterogeneous, multiscale nanotopography can enable more physiologically relevant platforms for studying neural ...
F. Maita, E. Palmieri, I. Lucarini, L. Montaina, L. Maiolo   +4 more
wiley   +1 more source

Diamond on Kurepa trees. [PDF]

Mon Hefte Math
Kostana Z, Rinot A, Shelah S.
europepmc   +1 more source

Physics‐Informed Machine Learning for Sustainable Alloy Design: Toward a Recyclable Unified Q&P Steel

Advanced Science, EarlyView.
A physics‐informed property‐bridging framework links high‐throughput hardness screening to tensile performance in quenching and partitioning steels. By transferring metallurgically guided representations across properties, a single alloy composition is designed to achieve multiple strength grades through heat‐treatment tuning alone, offering a ...
Xiaolu Wei, Yong Li, Chenchong Wang, Lingyu Wang, Xiang Song, Keming Mao, Yu Zhang, Wei Xu   +7 more
wiley   +1 more source

Genetic Architecture and Sample Size Impact Relative Performance of Nonlinear Machine Learning and Standard Polygenic Risk Scores

Zhu J, Baousi A, Morris AP, Guo H.
europepmc   +1 more source