Privacy-preserving communication and power injection over vehicle networks and 5G smart grid slice

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A Score-based Geometric Model for Molecular Dynamics Simulations

Molecular dynamics (MD) has long been the \emph{de facto} choice for modeling complex atomistic systems from first principles, and recently deep learning become a popular way to accelerate it. Notwithstanding, preceding approaches depend on intermediate variables such as the potential energy or force fields to update atomic positions, which requires additional computations to perform back-propagation. To waive this requirement, we propose a novel model called ScoreMD by directly estimating the gradient of the log density of molecular conformations. Moreover, we analyze that diffusion processes highly accord with the principle of enhanced sampling in MD simulations, and is therefore a perfect match to our sequential conformation generation task. That is, ScoreMD perturbs the molecular structure with a conditional noise depending on atomic accelerations and employs conformations at previous timeframes as the prior distribution for sampling. Another challenge of modeling such a conformation generation process is that the molecule is kinetic instead of static, which no prior studies strictly consider. To solve this challenge, we introduce a equivariant geometric Transformer as a score function in the diffusion process to calculate the corresponding gradient. It incorporates the directions and velocities of atomic motions via 3D spherical Fourier-Bessel representations. With multiple architectural improvements, we outperforms state-of-the-art baselines on MD17 and isomers of C7O2H10. This research provides new insights into the acceleration of new material and drug discovery

Ultrafast Charge Transfer in Atomically Thin MoS2/WS2 Heterostructures

Van der Waals heterostructures have recently emerged as a new class of materials, where quantum coupling between stacked atomically thin two-dimensional (2D) layers, including graphene, hexagonal-boron nitride, and transition metal dichalcogenides (MX2), give rise to fascinating new phenomena. MX2 heterostructures are particularly exciting for novel optoelectronic and photovoltaic applications, because 2D MX2 monolayers can have an optical bandgap in the near-infrared to visible spectral range and exhibit extremely strong light-matter interactions. Theory predicts that many stacked MX2 heterostructures form type-II semiconductor heterojunctions that facilitate efficient electron-hole separation for light detection and harvesting. Here we report the first experimental observation of ultrafast charge transfer in photo-excited MoS2/WS2 heterostructures using both photoluminescence mapping and femtosecond (fs) pump-probe spectroscopy. We show that hole transfer from the MoS2 layer to the WS2 layer takes place within 50 fs after optical excitation, a remarkable rate for van der Waals coupled 2D layers. Such ultrafast charge transfer in van der Waals heterostructures can enable novel 2D devices for optoelectronics and light harvesting

Amplitude- and phase-resolved nano-spectral imaging of phonon polaritons in hexagonal boron nitride

Phonon polaritons are quasiparticles resulting from strong coupling of photons with optical phonons. Excitation and control of these quasiparticles in 2D materials offer the opportunity to confine and transport light at the nanoscale. Here, we image the phonon polariton (PhP) spectral response in thin hexagonal boron nitride (hBN) crystals as a representative 2D material using amplitude- and phase-resolved near-field interferometry with broadband mid-IR synchrotron radiation. The large spectral bandwidth enables the simultaneous measurement of both out-of-plane (780 cm-1) and in-plane (1370 cm-1) hBN phonon modes. In contrast to the strong and dispersive in-plane mode, the out-of-plane mode PhP response is weak. Measurements of the PhP wavelength reveal a proportional dependence on sample thickness for thin hBN flakes, which can be understood by a general model describing two-dimensional polariton excitation in ultrathin materials

Investigating Graph Structure Information for Entity Alignment with Dangling Cases

Entity alignment (EA) aims to discover the equivalent entities in different knowledge graphs (KGs), which play an important role in knowledge engineering. Recently, EA with dangling entities has been proposed as a more realistic setting, which assumes that not all entities have corresponding equivalent entities. In this paper, we focus on this setting. Some work has explored this problem by leveraging translation API, pre-trained word embeddings, and other off-the-shelf tools. However, these approaches over-rely on the side information (e.g., entity names), and fail to work when the side information is absent. On the contrary, they still insufficiently exploit the most fundamental graph structure information in KG. To improve the exploitation of the structural information, we propose a novel entity alignment framework called Weakly-Optimal Graph Contrastive Learning (WOGCL), which is refined on three dimensions : (i) Model. We propose a novel Gated Graph Attention Network to capture local and global graph structure similarity. (ii) Training. Two learning objectives: contrastive learning and optimal transport learning are designed to obtain distinguishable entity representations via the optimal transport plan. (iii) Inference. In the inference phase, a PageRank-based method is proposed to calculate higher-order structural similarity. Extensive experiments on two dangling benchmarks demonstrate that our WOGCL outperforms the current state-of-the-art methods with pure structural information in both traditional (relaxed) and dangling (consolidated) settings. The code will be public soon

Pre-training of Equivariant Graph Matching Networks with Conformation Flexibility for Drug Binding

The latest biological findings observe that the traditional motionless 'lock-and-key' theory is not generally applicable because the receptor and ligand are constantly moving. Nonetheless, remarkable changes in associated atomic sites and binding pose can provide vital information in understanding the process of drug binding. Based on this mechanism, molecular dynamics (MD) simulations were invented as a useful tool for investigating the dynamic properties of a molecular system. However, the computational expenditure limits the growth and application of protein trajectory-related studies, thus hindering the possibility of supervised learning. To tackle this obstacle, we present a novel spatial-temporal pre-training method based on the modified Equivariant Graph Matching Networks (EGMN), dubbed ProtMD, which has two specially designed self-supervised learning tasks: an atom-level prompt-based denoising generative task and a conformation-level snapshot ordering task to seize the flexibility information inside MD trajectories with very fine temporal resolutions. The ProtMD can grant the encoder network the capacity to capture the time-dependent geometric mobility of conformations along MD trajectories. Two downstream tasks are chosen, i.e., the binding affinity prediction and the ligand efficacy prediction, to verify the effectiveness of ProtMD through linear detection and task-specific fine-tuning. We observe a huge improvement from current state-of-the-art methods, with a decrease of 4.3% in RMSE for the binding affinity problem and an average increase of 13.8% in AUROC and AUPRC for the ligand efficacy problem. The results demonstrate valuable insight into a strong correlation between the magnitude of conformation's motion in the 3D space (i.e., flexibility) and the strength with which the ligand binds with its receptor