Results 131 to 140 of about 15,908 (261)

Linear Hamiltonian Systems [PDF]

, 2003
openaire   +1 more source

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

Effective Mode Approximation for Probabilistic Verification of Collective Hamiltonians in Large Continuous-Variable Quantum Systems. [PDF]

Entropy (Basel)
Rosas-Bustos JR, Van Griensven Thé J, Fraser RA, Said N, Ratto Valderrama S, Pecen M, Truskovsky A, Thanos A.   +7 more
europepmc   +1 more source

Optimal Bandwidth in Semiconducting Polymer Chains

Advanced Electronic Materials, EarlyView.
Polymeric semiconductors have achieved improved mobility over the years while decreasing their bandwidth, a trend that clearly cannot continue indefinitely. This paper establishes the minimum bandwidth beyond which further band narrowing has a detrimental effect.
Suryoday Prodhan, Alessandro Troisi
wiley   +1 more source

Ab Initio Polariton Spectra of ZnTPP Molecules Collectively Coupled Inside an Optical Cavity. [PDF]

J Am Chem Soc
Weight BM, Rury AS, Shao Y, Huo P.
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

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

Dynamic Behavior of Mass Sensor Based on Switchable Dual-Mode Composite Strips. [PDF]

Sensors (Basel)
Xu Y, Bi H.
europepmc   +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

Oscillator-qubit generalized quantum signal processing: a case study of the uracil cation vibronic model. [PDF]

Chem Sci
Hong J, Kim SH, Min SK, Huh J.
europepmc   +1 more source