DeePMD-kit v2: A software package for Deep Potential models

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 figure

Effects of Product Substitutability and Power Relationships on Performance in Triadic Supply Chains

With the development of the retail industry and the increasing complexity of the power relationship between common retailers and manufacturers, research on product substitutability has become increasingly critical for operational management and decision-making regarding substitutable products. We investigated the effects of product substitutability on retail prices, profits, and social welfare for a triadic supply chain comprised of a retailer and two competing manufacturers. We then extended our analysis to the Nash bargaining game to evaluate the impact of product substitutability and bargaining power on equilibrium in a multiunit bilateral negotiation. The findings revealed that product homogeneity can harm the profits of manufacturers and the overall supply chain. In contrast, product substitutability’s impact on the profits of retailers depends on the inter-firm power relationship. Moreover, the retailer’s profit was found to consistently increase with respect to substitutability in a manufacturer Stackelberg model, but not so in the vertical Nash and retailer Stackelberg models. We also explored the effect of power structure on supply chain performance. Our results provide valuable insights that can help manufacturers and retailers decide on pricing, sourcing, and brand positioning to improve economic and social performance, as well as assist the government in deciding whether product differentiation should be encouraged

Influence of Multi-Layered Structure of Vadose Zone on Ecological Effect of Groundwater in Arid Area: A Case Study of Shiyang River Basin, Northwest China

The natural vegetation in arid areas of northwest China is strongly dependent on the availability of groundwater. Significantly, capillary water plays an essential role in regulating the ecological groundwater level in the multilayered structure of the vadose zone. The soil-column test and field survey in the lower reaches of the Shiyang River Basin were conducted to investigate the influence of the multi-layered structure of the vadose zone on maintaining the ecological effect of groundwater. Based on the field survey, the results show that the depth of groundwater is 3.0 m, and the rising height of capillary water is 140 cm. In the soil-column test, the height of the wetting front of the column was 125 cm. During the water releasing test, the water held by the vadose zone was 182.54 mm, which would have maintained Haloxylon’s survival in a growing season. Therefore, the multi-layered structure of the vadose zone extends the ecological groundwater depth and consequently enhances the ecological function of groundwater. Importantly, with a lower groundwater level, the clay soil layer within the rising height range of the original capillary water would hold more water and maintain a higher water content for a certain period to supply surface vegetation

An Approach to Study Groundwater Flow Field Evolution Time Scale Effects and Mechanisms

The temporal scale effect is an important issue for groundwater system evolution research. The selection of an appropriate time scale will enhance the understanding of the characteristics and mechanisms of groundwater flow field evolution. In this study, a methodology was provided to analyze the groundwater system evolution, focusing on the choice of the suitable time step for identifying the distinct stages of evolution, characterized by different behavior linked to the management of the groundwater system. The evolution trend of the groundwater level in the center of the cone of depression at different time scales, combined with the F test and the groundwater system balance index (Re) categories, were used for the choice of the time step and the division of the evolution stages. Based on the transformed groundwater level time series using the selected best time step, the main factors controlling the groundwater evolution were assessed for the different stages. Our results show that the methodology can exactly identify the different important stages of the evolution, and they can be used to individually study these stages, which can help to reveal the mechanisms of the groundwater evolution more easily. Therefore, it is useful to obtain an increased knowledge of the regional groundwater dynamics

Explore the Chemical Space of Linear Alkanes Pyrolysis via Deep Potential Generator

Reactive molecular dynamics (MD) simulation is a powerful tool to study the reaction mechanism of complex chemical systems. Central to the method is the potential energy surface (PES) that can describe the breaking and formation of chemical bonds. The development of PES of both accurate and efficent has attracted significant effort in the past two decades. Recently developed Deep Potential (DP) model has the promise to bring ab initio accuracy to large-scale reactive MD simulations. However, for complex chemical reaction processes like pyrolysis, it remains challenging to generate reliable DP models with an optimal training dataset. In this work, a dataset construction scheme for such a purpose was established. The employment of a concurrent learning algorithm allows us to maximize the exploration of the chemical space while minimize the redundancy of the dataset. This greatly reduces the cost of computational resources required by ab initio calculations. Based on this method, we constructed a dataset for the pyrolysis of n-dodecane, which contains 35,496 structures. The reactive MD simulation with the DP model trained based on this dataset revealed the pyrolysis mechanism of n-dodecane in detail, and the simulation results are in good agreement with the experimental measurements. In addition, this dataset shows excellent transferability to different long-chain alkanes. These results demonstrate the advantages of the proposed method for constructing training datasets for similar systems. </div

Comparative investigation of the deactivation behaviors over HZSM-5 and HSAPO-34 catalysts during low-temperature methanol conversion

The deactivation mechanism for the methanol conversion reaction at low temperature was comparatively investigated over HZSM-5 and HSAPO-34 catalysts. Two obviously different deactivation phenomena were directly observed: two-staged deactivation behavior over the HZSM-5 catalyst and exponential- type deactivation behavior over the HSAPO-34 catalyst. Since the start of the deactivation, the amount of the retained species over the HZSM-5 catalyst kept unchanged while the amount over the HSAPO-34 catalyst obviously increased. Both types of deactivation behavior presented an intimate relationship with the accumulation of retained species and their changing reactivity. After detailed characterization and analysis, it was interestingly found that the deactivation of the HZSM-5 catalyst originated from the "overloading effect" of methylbenzenes(smaller than pentamethylbenzene) which are intrinsically active during the autocatalysis reaction stage, while the deactivation of the HSAPO-34 catalyst was caused by accumulation of inactive methyladamantanes, and it was further deduced that the deactivation proceeded from "external to internal" for the HSAPO-34 catalyst. Enhancement of the catalyst diffusivity could effectively extend the catalyst lifetime for the HZSM-5 catalyst, but seemed less effective for the HSAPO-34 catalyst