相关论文: Hybrid neural network potential for multilayer gra…
The wettability of monolayer and multilayer graphene remains a topic of longstanding debate. Here, we combined first-principles molecular dynamics simulations accelerated with the atomic cluster expansion machine learning interatomic…
This work introduces the multidimensional Graph Fourier Transformation Neural Network (GFTNN) for long-term trajectory predictions on highways. Similar to Graph Neural Networks (GNNs), the GFTNN is a novel network architecture that operates…
The interlayer interaction in graphene/boron-nitride heterostructures is studied using density functional theory calculations with the correction for van der Waals interactions. It is shown that the use of the experimental interlayer…
Graph Neural Networks have emerged as an effective machine learning tool for multi-disciplinary tasks such as pharmaceutical molecule classification and chemical reaction prediction, because they can model non-euclidean relationships…
Lattice dynamics in low-dimensional materials and, in particular, the quadratic behaviour of the flexural acoustic modes play a fundamental role in their thermomechanical properties. A first-principles evaluation of these can be very…
Modern microelectronic devices are composed of interfaces between a large number of materials, many of which are in amorphous or polycrystalline phases. Modeling such non-crystalline materials using first-principles methods such as density…
We develop a pairing-based graph neural network for simulating quantum many-body systems. Our architecture augments a BCS-type geminal wavefunction with a generalized pair amplitude parameterized by a graph neural network. Variational Monte…
Graphene-derived nanomaterials are emerging as ideal candidates for postsilicon electronics. Elucidating the electronic interaction between an insulating substrate and few-layer graphene (FLG) films is crucial for device applications. Here,…
The intertwining relations between superpartner Hamiltonians are the main ingredients of well known Supersymmetrical Quantum Mechanics (SUSY QM). In the present paper, the generalized form of intertwining is used for investigation of a…
Graphene plasmons were predicted to possess ultra-strong field confinement and very low damping at the same time, enabling new classes of devices for deep subwavelength metamaterials, single-photon nonlinearities, extraordinarily strong…
Machine learning interatomic potentials (MLIPs) offer an efficient and accurate framework for large-scale molecular dynamics (MD) simulations, effectively bridging the gap between classical force fields and \textit{ab initio} methods. In…
Porous media is widely distributed in nature, found in environments such as soil, rock formations, and plant tissues, and is crucial in applications like subsurface oil and gas extraction, medical drug delivery, and filtration systems.…
This paper investigates tunable magneto-plasmons in graphene-based structures combined with gyro-electric layers. In the general waveguide, each graphene sheet has been sandwiched between two different gyro-electric layers. The whole…
Heterogeneous graph convolutional networks have gained great popularity in tackling various network analytical tasks on heterogeneous network data, ranging from link prediction to node classification. However, most existing works ignore the…
We study the atomic structure of twisted bilayer graphene, with very small mismatch angles ($\theta \sim 0.28^0$), a topic of intense recent interest. We use simulations, in which we combine a recently presented semi-empirical potential for…
Condense phase molecular systems organize in wide range of distinct molecular configurations, including amorphous melt and glass as well as crystals often exhibiting polymorphism, that originate from their intricate intra- and…
We have performed a first-principles density functional theory investigation of the penetration of helium atoms through a graphene monolayer with defects. The relaxation of the graphene layer caused by the incoming helium atoms does not…
Machine learning (ML) methods have drawn significant interest in material design and discovery. Graph neural networks (GNNs), in particular, have demonstrated strong potential for predicting material properties. The present study proposes a…
Close to charge neutrality, the low-energy properties of high-quality suspended devices based on atomically thin graphene layers are determined by electron-electron interactions. Bernal-stacked layers, in particular, have shown a remarkable…
Graphene can support surface plasmons with higher confinement, lower propagation loss, and substantially more tunable response compared to usual metal-based plasmonic structures. Interestingly, plasmons in graphene can strongly couple with…