Results 131 to 140 of about 24,728 (260)

Remarkable Electric Field‐Controlled Magnetism in La/Sm Co‐Substituted BiFeO3 Multiferroic Ceramics

Advanced Electronic Materials, EarlyView.
The remarkable electric field‐controlled magnetism (change of Mr reaches up to 62.7%) in La/Sm co‐substituted BiFeO3 ceramics, originating from the electric field‐induced phase transition of Pna21/R3c, is achieved by enhancing the dielectric strength. Moreover, the significant improvement in ferroelectricity is also determined after the electric field ...
Qing Tian Li, Xiao Li Zhu, Xiao Qiang Liu, Xiang Ming Chen   +3 more
wiley   +1 more source

Geometry of the Kahan discretizations of planar quadratic Hamiltonian systems. [PDF]

Proc Math Phys Eng Sci, 2019
Petrera M, Smirin J, Suris YB.
europepmc   +1 more source

Discovery of an Ideal HgO12 Icosahedron and Magnetodielectric Coupling in HgCu3Ti4O12

Advanced Electronic Materials, EarlyView.
HgCu3Ti4O12, an A‐site‐ordered quadruple perovskite with a perfectly regular HgO12 coordination, has been synthesized for the first time using high‐pressure methods. The material shows antiferromagnetic ordering at 31 K together with an intrinsic dielectric anomaly, implying possible magnetodielectric coupling.
Haoyu Zheng, Jianfa Zhao, Jun Deng, Lei Duan, Xudong Shen, Zhiwei Hu, Luchuan Shi, Yuling Dai, Xiaoming Chen, Xubin Ye, Jun Zhang, Wenmin Li, Zheng Deng, Xiancheng Wang, Chang‐Yang Kuo, Chien‐Te Chen, Youwen Long, Runze Yu, Changjiang Xiao, Changqing Jin   +19 more
wiley   +1 more source

Combinatorial optimization by simulating adiabatic bifurcations in nonlinear Hamiltonian systems. [PDF]

Sci Adv, 2019
Goto H, Tatsumura K, Dixon AR.
europepmc   +1 more source

Prediction of Structural Stability of Layered Oxide Cathode Materials: Combination of Machine Learning and Ab Initio Thermodynamics

Advanced Energy Materials, EarlyView.
In this work, we developed a phase‐stability predictor by combining machine learning and ab initio thermodynamics approaches, and identified the key factors determining the favorable phase for a given composition. Specifically, a lower TM ionic potential, higher Na content, and higher mixing entropy favor the O3 phase.
Liang‐Ting Wu, Milica Zdravković, Dijana Milosavljević, Konstantin Köster, Olivier Guillon, Jyh‐Chiang Jiang, Payam Kaghazchi   +6 more
wiley   +1 more source

Reducible problem for a class of almost-periodic non-linear Hamiltonian systems. [PDF]

J Inequal Appl, 2018
Afzal M, Ismaeel T, Jamal M.
europepmc   +1 more source

Machine Learning Interatomic Potentials for Energy Materials: Architectures, Training Strategies, and Applications

Advanced Energy Materials, EarlyView.
Machine learning interatomic potentials bridge quantum accuracy and computational efficiency for materials discovery. Architectures from Gaussian process regression to equivariant graph neural networks, training strategies including active learning and foundation models, and applications in solid‐state electrolytes, batteries, electrocatalysts ...
In Kee Park, Saeed Pourasad, Jinhong Mun, Arunkumar Chitteth Rajan, Tae Hyeon Park, Anand Rohit, Mohammad Zafari, Tan‐Lien Pham, Eunjae Jung, Bonseung Koo, Miran Ha, Gi Beom Sim, Hyun Gyu Park, Won Woong Choi, Kwanghyeon Jo, Soohaeng Yoo Willow, David ChangMo Yang, Chang Woo Myung, Geunsik Lee, Kwang S. Kim   +19 more
wiley   +1 more source

Intrinsic Quantization of Linear Hamiltonian Systems. [PDF]

Entropy (Basel)
Accardi L, Pandiscia C.
europepmc   +1 more source

Periodic Solutions of Asymptotically Linear Autonomous Hamiltonian Systems with Resonance. [PDF]

J Dyn Differ Equ, 2018
Gołȩbiewska A.
europepmc   +1 more source

Mechanisms of Alkali Ionic Transport in Amorphous Oxyhalides Solid State Conductors

Advanced Energy Materials, EarlyView.
Large‐scale machine learning‐based molecular dynamics simulations are used to investigate isovalent amorphous oxyhalides, revealing a remarkable chemically independent ionic conductivity. A rigorous analysis of alkali residence times across different metal–anion environments identifies divalent anions as key diffusion bottlenecks.
Luca Binci, KyuJung Jun, Bowen Deng, Gerbrand Ceder   +3 more
wiley   +1 more source