Results 31 to 40 of about 1,719 (142)

Polarization Dynamics in Ferroelectrics: Insights Enabled by Machine Learning Molecular Dynamics

Advanced Science, EarlyView.
Machine learning molecular dynamics is presented as a route to capture polarization switching, domain wall kinetics, topological polar textures, and polar mechanical coupling beyond the limits of conventional atomistic methods. This Perspective surveys recent progress and identifies key methodological directions, including long‐range electrostatics ...
Dongyu Bai, Ri He, Junxian Liu, Liangzhi Kou   +3 more
wiley   +1 more source

The Effect of Depth of Cut on the Molecular Dynamics (MD) Simulation of Multi-Pass Nanometric Machining [PDF]

, 2011
The effect of depth of cut on multi-pass nanometric machining of copper workpiece with diamond tool was studied using the Molecular Dynamics (MD) simulation.
Oluwajobi, Akinjide O., Chen, Xun
core  

Advancing Energy Materials by In Situ Atomic Scale Methods

Advanced Energy Materials, Volume 15, Issue 11, March 18, 2025.
Progress in in situ atomic scale methods leads to an improved understanding of new and advanced energy materials, where a local understanding of complex, inhomogeneous systems or interfaces down to the atomic scale and quantum level is required. Topics from photovoltaics, dissipation losses, phase transitions, and chemical energy conversion are ...
Christian Jooss, Michael Seibt, Martin Wenderoth, Oliver Bünermann, Ole Bunjes, Till Domröse, Christian Eckel, Francesca Falorsi, Christoph Flathmann, Monica Kolek Martinez de Azagra, Matthias Krüger, Jonas Lindner, Tobias Meyer, Claus Ropers, Ulrich Ross, Kai Rossnagel, Sreeju Sreekantan Nair Lalithambika, Simone Techert, Georg A. Traeger, Cynthia Volkert, R. Thomas Weitz, Alec M. Wodtke   +21 more
wiley   +1 more source

Empirical interatomic potential for the mechanical, vibrational and thermodynamic properties of semiconductors [PDF]

, 2012
Empirical models are widely used to simulate large atomic structures where instead ab initio methods are not practical because of computational limitations.
Migliorato, M A, Monteverde, U., Powell, D, Monteverde, U, Powell, D., Migliorato, M. A.   +5 more
core   +1 more source

Sulfide‐Based Electrolytes for All‐Solid‐State Sodium Batteries

Advanced Energy Materials, EarlyView.
This review covers the structural features and synthesis strategies of sulfide‐based solid electrolytes, as well as critical challenges related to conductivity, interfacial and moisture stability, and scaling‐up for practical application in Sodium‐based All Solid‐State Batteries.
Han Yang, Luca Weckelmann, Baolin Wu, Chih‐Long Tsai, Luc Raijmakers, Rüdiger‐A. Eichel, Anna Windmüller   +6 more
wiley   +1 more source

Empirical Interatomic Potentials for L1_O Tial and B2 Nial [PDF]

, 1990
Recent studies have suggested a particular relationship between the degree of covalent bonding in TiAl and the mobility of dislocation[1,2]. Ultimately such electronic effects In ordered compounds must dictate the dislocation core structures and at the ...
Satish I. Rao, C. Woodward, T.A. Parthasarathy   +2 more
core   +1 more source

Emerging Materials and Future Strategies for Solid Oxide Electrochemical Cells

Advanced Energy Materials, EarlyView.
Solid oxide electrochemical cells operate under strongly coupled electrochemical and thermodynamic conditions, where performance is constrained by interactions among crystal structure, defect chemistry, and interfacial evolution. This review, based on a structure‐defect‐property‐durability framework, reveals the roles of lattice symmetry and defect ...
Qiuchun Lu, Yufei Zhang, Han Zhao, Pooi See Lee, Daniel H.C. Chua   +4 more
wiley   +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

Harnessing Machine Learning to Understand and Design Disordered Solids

Advanced Intelligent Discovery, EarlyView.
This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley   +1 more source

Modified embedded-atom interatomic potential for Co-W and Al-W systems [PDF]

, 2015
A semi-empirical interatomic potential formalism, the second-nearest-neighbor modified embedded-atom method (2NN MEAM), has been applied to obtaining interatomic potentials for the Co-W and Al-W binary system using previously developed MEAM potentials of
Chen, Z, Dong, WP, Lee, BJ
core   +1 more source