Results 161 to 170 of about 17,286,981 (307)

Electric Field‐Induced Hole‐ and Electron‐Type Flat Bands in Twisted Double Bilayer Graphene

Advanced Electronic Materials, EarlyView.
The electronic structure of twisted double bilayer graphene is visualized using angle‐resolved photoemission spectroscopy with micrometer spatial resolution at twists of 3.1∘$^\circ$ and 6.0∘$^\circ$ as a function of gate voltage. Tunable hybridization effects and flat band formation occurs between valence and conduction band states due to a finite ...
Zhihao Jiang, Dongkyu Lee, Alfred J. H. Jones, Youngju Park, Paulina Majchrzak, Kimberly Hsieh, Chakradhar Sahoo, Thomas S. Nielsen, Kenji Watanabe, Takashi Taniguchi, Philip Hofmann, Yong P. Chen, Jeil Jung, Søren Ulstrup   +13 more
wiley   +1 more source

The heteroclinic connection problem for general double-well potentials [PDF]

, 2014
By variational methods, we provide a simple proof of existence of a heteroclinic orbit to a second order Hamiltonian ODE that connects the two global minima of a double-well potential.
Sourdis, Christos
core  

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

Coarse-graining Hamiltonian systems using WSINDy

Scientific Reports
Weak form equation learning and surrogate modeling has proven to be computationally efficient and robust to measurement noise in a wide range of applications including ODE, PDE, and SDE discovery, as well as in coarse-graining applications, such as ...
Daniel A. Messenger, Joshua W. Burby, David M. Bortz   +2 more
doaj   +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

Competing Charge Separation Pathways Govern Charge Generation in Organic Photovoltaic Blends: Insights From Transient Electron Spin Resonance

Advanced Energy Materials, EarlyView.
Transient electron spin resonance distinguishes interfacial charge‐transfer states from separated charges, revealing competing charge separation pathways in organic photovoltaic blends. The relative contributions of fast charge separation and a pathway mediated by spin‐polarized charge‐transfer states vary across different blends, with high‐efficiency ...
Jack M. S. Palmer, Claudia E. Tait
wiley   +1 more source

Hamiltonian systems

Scholarpedia, 2007
openaire   +1 more source

Limitations of Foundation Models in Energy Materials Simulations: A Case Study in Polyanion Sodium Cathode Materials

Advanced Intelligent Discovery, EarlyView.
Several simulation techniques are used to explore static and dynamic behavior in polyanion sodium cathode materials. The study reveals that universal machine learning interatomic potentials (MLIPs) struggle with system‐specific chemistry, emphasizing the need for tailored datasets.
Martin Hoffmann Petersen, Chiku Parida, Jonas Busk, Jin Hyun Chang, Arghya Bhowmik, Juan Maria Garcia Lastra   +5 more
wiley   +1 more source

Factorization Machine‐Based Active Learning for Functional Materials Design with Optimal Initial Data

Advanced Intelligent Discovery, EarlyView.
This work investigates the optimal initial data size for surrogate‐based active learning in functional material optimization. Using factorization machine (FM)‐based quadratic unconstrained binary optimization (QUBO) surrogates and averaged piecewise linear regression, we show that adequate initial data accelerates convergence, enhances efficiency, and ...
Seongmin Kim, In‐Saeng Suh
wiley   +1 more source

C1 non-integrability of a hydrogen atom in a circularly polarized microwave field

Open Physics, 2012
Guirao Juan, López Miguel, Vera Juan
doaj   +1 more source