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

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

Development of Range-Corrected Deep Learning Potentials for Fast, Accurate Quantum Mechanical/molecular Mechanical Simulations of Chemical Reactions in Solution

We develop a new Deep Potential - Range Correction (DPRc) machine learning potential for combined quantum mechanical/molecular mechanical (QM/MM) simulations of chemical reactions in the condensed phase. The new range correction enables short-ranged QM/MM interactions to be tuned for higher accuracy, and the correction smoothly vanishes within a specified cutoff. We further develop an active learning procedure for robust neural network training. We test the DPRc model and training procedure against a series of 6 non-enzymatic phosphoryl transfer reactions in solution that are important in mechanistic studies of RNA-cleaving enzymes. Specifically, we apply DPRc corrections to a base QM model and test its ability to reproduce free energy profiles generated from a target QM model. We perform comparisons using the MNDO/d and DFTB2 semiempirical models because they produce free energy profiles which differ significantly from each other, thereby providing us a rigorous stress test for the DPRc model and training procedure. The comparisons show that accurate reproduction of the free energy profiles requires correction of the QM/MM interactions out to 6 Å. We further find that the model\u27s initial training benefits from generating data from temperature replica exchange simulations and including high-temperature configurations into the fitting procedure so the resulting models are trained to properly avoid high-energy regions. A single DPRc model was trained to reproduce 4 different reactions and yielded good agreement with the free energy profiles made from the target QM/MM simulations. The DPRc model was further demonstrated to be transferable to 2D free energy surfaces and 1D free energy profiles that were not explicitly considered in the training. Examination of the computational performance of the DPRc model showed that it was fairly slow when run on CPUs, but was sped up almost 100-fold when using an NVIDIA V100 GPUs, resulting in almost negligible overhead. The new DPRc model and training procedure provide a potentially powerful new tool for the creation of next-generation QM/MM potentials for a wide spectrum of free energy applications ranging from drug discovery to enzyme design.<br /

Extremely Sensitive Anomaly Detection in Pipe Networks Using a Higher-Order Paired-Impulse Response Function with a Correlator

Detection of anomalies in pipe networks (leaks, blockages, and wall deterioration) is critical for targeted pipe section replacement and maintenance in water distribution systems. A hydraulic signal-processing approach, termed the paired-impulse response function (paired-IRF), has been previously proposed for anomaly detection by transforming the persistent principal wave reflections by anomalies into distinctive paired spikes. In this paper, a new higher-order paired-IRF has been derived, which considers both principal and higher-order wave reflections by the anomalies. A correlator has then been designed (and incorporated into the higher-order paired-IRF) to highlight anomaly-induced spikes and suppress noise. A looped pipe network with realistic background noise was assembled in the laboratory to examine the efficacy of the new methods. According to the experimental results, it is observed that (1) the higher-order paired-IRF is an extremely sensitive detection technique and clearly identifies anomalies inducing wave reflections as small as 0.5% of the injected wave magnitude; (2) its sensitivity is sufficiently accurate when using micropressure waves as small as 20 mm in magnitude and contaminated by 2-m background pressure fluctuations; and (3) the proposed advanced correlator highlights the anomaly-induced spikes in the paired-IRF trace in a noisy environment.Wei Zeng, Aaron C. Zecchin, Benjamin S. Cazzolato, Angus R. Simpson, Jinzhe Gong and Martin F. Lamber

ReacNetGenerator: an Automatic Reaction Network Generator for Reactive Molecular Dynamic Simulations

The reactive molecular dynamics is widely used in the field of computational chemistry to study the reaction mechanisms in molecular systems. However, complex trajectories that are difficult to analyze have become a major obstacle to its application in large-scale systems. In this work, a new approach named ReacNetGen is developed to obtain reaction networks based on reactive MD simulations. Molecular species can be automatically generated from the 3D coordinates of atoms in the trajectory. The hidden Markov model is used to filter the noises in the trajectory, which makes the analysis process easier and more accurate. Compared with manual analysis, the advantage of this method in terms of efficiency is very obvious for large-scale simulation trajectories. It has been successfully used in the analysis of the simulated oxidation of 4-component RP-3 and methane

Detection of extended blockages in pressurised pipelines using hydraulic transients with a layer-peeling method

Water distribution systems (WDSs) are one of society\u27s most important infrastructure assets. They consist of buried pipes that are often old and their condition is extremely difficult and expensive to determine. This research proposes a non-invasive layer-peeling method using hydraulic transient waves to detect extended blockages in pressurised pipelines. In the numerical study, hydraulic transient pressure waves are injected into a pipeline at a dead-end. Wave reflections caused by multiple extended blockages (uniform and non-uniform) are simulated using the method of characteristics (MOC). The impulse response function (IRF) of the pipeline is then obtained using the simulated pressure response at the dead-end. The original layer-peeling method previously applied to tubular music instruments is further developed by considering the differences between the instruments and pressurised pipelines (boundary conditions, fluid properties). Using the IRF and the modified layer-peeling method, the internal pipe diameter values are estimated section by section from the dead-end to the upstream end of the pipeline. The blocked pipe sections are then accurately identified from the reconstructed pipe wall thickness distribution profile

Combustion Driven by Fragment-based Ab Initio Molecular Dynamics Simulation

Combustion is a kind of important reaction that affects people\u27s daily lives and the development of aerospace. Exploring the reaction mechanism contributes to the understanding of combustion and the more efficient use of fuels. Ab initio quantum mechanical (QM) calculation is precise but limited by its computational time for large-scale systems. In order to carry out reactive molecular dynamics (MD) simulation for combustion accurately and quickly, we develop the MFCC-combustion method in this study, which calculates the interaction between atoms using QM method at the level of MN15/6-31G(d). Each molecule in systems is treated as a fragment, and when the distance between any two atoms in different molecules is greater than 3.5 Å, a new fragment involved two molecules is produced in order to consider the two-body interaction. The deviations of MFCC-combustion from full system calculations are within a few kcal/mol, and the result clearly shows that the calculated energies of the different systems using MFCC-combustion are close to converging after the distance thresholds are larger than 3.5 Å for the two-body QM interactions. The methane combustion was studied with the MFCC-combustion method to explore the combustion mechanism of the methane-oxygen system

Fragment-Based Ab Initio Molecular Dynamics Simulation for Combustion

We develop a fragment-based ab initio molecular dynamics (FB-AIMD) method for efficient dynamics simulation of the combustion process. In this method, the intermolecular interactions are treated by a fragment-based many-body expansion in which three- or higher body interactions are neglected, while two-body interactions are computed if the distance between the two fragments is smaller than a cutoff value. The accuracy of the method was verified by comparing FB-AIMD calculated energies and atomic forces of several different systems with those obtained by standard full system quantum calculations. The computational cost of the FB-AIMD method scales linearly with the size of the system, and the calculation is easily parallelizable. The method is applied to methane combustion as a benchmark. Detailed reaction network of methane reaction is analyzed, and important reaction species are tracked in real time. The current result of methane simulation is in excellent agreement with known experimental findings and with prior theoretical studies