Search

The rapid advancements in artificial intelligence (AI) are catalyzing transformative changes in atomic modeling, simulation, and design. AI-driven potential energy models have demonstrated the capability to conduct large-scale, long-duration simulations with the accuracy of ab initio electronic structure methods. However, the model generation process remains a bottleneck for large-scale applications. We propose a shift towards a model-centric ecosystem, wherein a large atomic model (LAM), pre-trained across multiple disciplines, can be efficiently fine-tuned and distilled for various downstream tasks, thereby establishing a new framework for molecular modeling. In this study, we introduce the DPA-2 architecture as a prototype for LAMs. Pre-trained on a diverse array of chemical and materials systems using a multi-task approach, DPA-2 demonstrates superior generalization capabilities across multiple downstream tasks compared to the traditional single-task pre-training and fine-tuning methodologies. Our approach sets the stage for the development and broad application of LAMs in molecular and materials simulation research.

DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials (MLP) known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used in the fields of physics, chemistry, biology, and material science for studying atomistic systems. The current version of DeePMD-kit offers numerous advanced features such as DeepPot-SE, attention-based and hybrid descriptors, the ability to fit tensile properties, type embedding, model deviation, Deep Potential - Range Correction (DPRc), Deep Potential Long Range (DPLR), GPU support for customized operators, model compression, non-von Neumann molecular dynamics (NVNMD), and improved usability, including documentation, compiled binary packages, graphical user interfaces (GUI), and application programming interfaces (API). This article presents an overview of the current major version of the DeePMD-kit package, highlighting its features and technical details. Additionally, the article benchmarks the accuracy and efficiency of different models and discusses ongoing developments., Comment: 51 pages, 2 figures

Recently, the development of machine learning (ML) potentials has made it possible to perform large-scale and long-time molecular simulations with the accuracy of quantum mechanical (QM) models. However, for high-level QM methods, such as density functional theory (DFT) at the meta-GGA level and/or with exact exchange, quantum Monte Carlo, etc., generating a sufficient amount of data for training a ML potential has remained computationally challenging due to their high cost. In this work, we demonstrate that this issue can be largely alleviated with Deep Kohn-Sham (DeePKS), a ML-based DFT model. DeePKS employs a computationally efficient neural network-based functional model to construct a correction term added upon a cheap DFT model. Upon training, DeePKS offers closely-matched energies and forces compared with high-level QM method, but the number of training data required is orders of magnitude less than that required for training a reliable ML potential. As such, DeePKS can serve as a bridge between expensive QM models and ML potentials: one can generate a decent amount of high-accuracy QM data to train a DeePKS model, and then use the DeePKS model to label a much larger amount of configurations to train a ML potential. This scheme for periodic systems is implemented in a DFT package ABACUS, which is open-source and ready for use in various applications.

In the present paper, the Karhunen-Lo{\`e}ve eigenvalues for a sub-fractional Brownian motion are considered in the case of $H>\frac12$. Rigorous large $n$ asymptotics for those eigenvalues are shown, based on functional analysis method. By virtue of these asymptotics, along with some standard large deviations results, asymptotically estimates for the closely related problem of small $L^2$-ball probabilities for a sub-fractional Brownian motion are derived. By the way, asymptotic analysis on the Karhunen-Lo{\`e}ve eigenvalues for the corresponding "derivative" process is also established.

We propose a model, for the first time, to quantitatively estimate the intercore spontaneous Raman scattering (ICSRS) in multicore fber (MCF) based on the characterization of intercore crosstalk (ICXT). Also, we show the propertis of ICSRS through numerical simulations. Then the impact of ICSRS on quantum key distribution (QKD) is evaluated. It is revealed that both the forward-ICSRS and backwardICSRS will reduce the maximum transmisson distance and the forward-ICSRS will reduce more. However, over the range of metropolitan area networks, quantum signals are affected only when the powers of classical signals are very high in dense wavelength division multiplexing system. Finally, the spontaneous Raman scattering (SRS) generated in single core fiber and the ICSRS generated in MCF are compared. The power variety trend with the transmission distance of SRS is similar to that of ICSRS, though there are subtle differences.

We propose a quantum access network based on multicore fiber (MCF) to scale up the number of users in quantum key distribution (QKD) networks. The MCF is used as feeder fiber and single core single mode fibers (SSMFs) are used as drop fibers. Quantum signals (QSs) are integrated with classical signals (CSs) in both MCF and SSMFs to save deployment cost since access networks are cost sensitive. Due to the integration, spontaneous Raman scattering (SRS) and intercore crosstalk (ICXT) are the main impairment sources to QKD. To alleviate the noise, we propose a core and wavelength assignment scheme (CWAS) that a dedicated core of MCF is used to transmit QSs and the wavelengths of QSs are set lower than those of upstream signals. Also, we demonstrate that wavelength time division multiplexing (WTDM) is suitable for QSs which are required to support large number of quantum users, since WTDM can realize higher secure key rate (SKR) than time division multiplexing (TDM) and require lower cost than wavelength division multiplexing (WDM). Finally, the proposed quantum access network is verified experimentally. The experiment results are consistent with our analysis. The properties of SRS in the proposed architecture are shown in the experiments through the quantum bit error rates (QBERs) in different experimental case, which verifies the superiority of the proposed CWAS. Also, the characteristics of the SKRs prove that the number of receivers has a great impact on the performance of QSs using WTDM.

We demonstrate quantum key distribution (QKD) with classical signals in a seven-core fiber using dense wavelength division multiplexing. Quantum signals are transmitted in an outer core separately and intercore crosstalk (IC-XT) is the main impairment of them. In order to alleviate IC-XT, we propose a quantum-classical interleave scheme. Then the properties of IC-XT are analyzed based on the measurement results which indicate counter-propagation is a better co-existence method than co-propagation. Finally, we perform QKD experiments in the presence of two classical channels with a channel spacing of 100 GHz between quantum channel and the nearest classical channels. The experiment results prove counter-propagation almost immune to IC-XT, which is consistent with our analysis. Also, the feasibility of the transmission over the range of metropolitan area networks is validated with our scheme.