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- Cited by (133)
- Recommended articles (6)

## Computer Physics Communications

Volume 253,

August 2020

, 107206

Author links open overlay panelYuzhiZhangabHaidiWangcWeijieChendJinzheZengeLinfengZhangfPersonEnvelopeHanWanggPersonEnvelopeWeinanEafPersonEnvelope

## Abstract

In recent years, promising deep learning based interatomic potential energy surface (PES) models have been proposed that can potentially allow us to perform molecular dynamics simulations for large scale systems with quantum accuracy. However, making these models truly reliable and practically useful is still a very non-trivial task. A key component in this task is the generation of datasets used in model training. In this paper, we introduce the Deep Potential GENerator (DP-GEN), an open-source software platform that implements the recently proposed ”on-the-fly” learning procedure (Zhang et al. 2019) and is capable of generating uniformly accurate deep learning based PES models in a way that minimizes human intervention and the computational cost for data generation and model training. DP-GEN automatically and iteratively performs three steps: exploration, labeling, and training. It supports various popular packages for these three steps: LAMMPS for exploration, Quantum Espresso, VASP, CP2K, etc. for labeling, and DeePMD-kit for training. It also allows automatic job submission and result collection on different types of machines, such as high performance clusters and cloud machines, and is adaptive to different job management tools, including Slurm, PBS, and LSF. As a concrete example, we illustrate the details of the process for generating a general-purpose PES model for Cu using DP-GEN.

### Program summary

*Program Title:* DP-GEN

*Program Files doi:* http://dx.doi.org/10.17632/sxybkgc5xc.1

*Licensing provisions:* LGPL

*Programming language:* Python

*Nature of problem:* Generating reliable deep learning based potential energy models with minimal human intervention and computational cost.

*Solution method:* The concurrent learning scheme is implemented. Supports for sampling configuration space with LAMMPS, generating *ab initio* data with Quantum Espresso, VASP, CP2K and training potential models with DeePMD-kit are provided. Supports for different machines including workstations, high performance clusters and cloud machines are provided. Supports for job management tools including Slurm, PBS, LSF are provided.

## Introduction

In recent years, machine learning (ML) has emerged as a promising tool for the field of molecular modeling. In particular, ML-based models have been proposed to address a long-standing issue, the accuracy-*vs*-efficiency dilemma when one evaluates the potential energy surface (PES), a function of atomic positions and their chemical species, and its negative gradients with respect to the atomic positions, namely the interatomic forces. From a first-principles point of view, PES is derived from the many-particle Schrödinger equation under the Born–Oppenheimer approximation, and the interatomic forces are given naturally by the Hellman–Feynman theorem. To this end, the *ab initio* molecular dynamics (AIMD) scheme, wherein accurate PES and interatomic forces are obtained within the density functional theory (DFT) approximation, has been most widely adopted[1], [2], [3]. Unfortunately, the cost of AIMD restricts its typical applications to system sizes of hundreds of atoms and the time scale of $\sim $100ps. In the opposite direction, efficient empirical PES models, or force fields (FF), allow us to perform much larger and longer simulations; but their accuracy and transferability is often an issue. ML has the potential to change this situation: a good ML model trained on *ab initio* data should have an efficiency comparable with that of FF models in the sense that the costs of ML models and FF models both scale linearly with system size, while maintaining *ab initio* accuracy.

Developing ML-based PES models involves two components, data generation and model construction. To date, most discussions have focused on the second component. Two important issues are: A good functional form (e.g.kernel based models or neural networks) and respecting physical constraints of the PES, such as the extensiveness and symmetry properties. In this regard, two representative classes of models have emerged: the kernel-based models like the Gaussian Approximation Potential[4] and the neural network (DNN) based models like the Behler–Parrinello model[5] and the Deep Potential model[6], [7]. In particular, the smooth version of the Deep Potential model is an end-to-end model that satisfies the requirements mentioned above[8].

There have also been some efforts on open-source software along this line[9], [10], [11], [12]. Of particular relevance to this work is the DeePMD-kit package[10], which has been developed to minimize the effort required to build DNN-based PES models and to perform DNN-based MD simulation. DeePMD-kit is interfaced with TensorFlow[13], one of the most popular deep learning frameworks, making the training process highly automatic and efficient. DeePMD-kit is also interfaced with popular MD packages, such as the LAMMPS package[14] for classical MD and the i-PI package[15] for path integral MD. Thus, once one has a good sets of data, there are now effective tools for training Deep Potentials that can be readily used to perform efficient molecular dynamics simulation for all different kinds of purposes.

In comparison, much less effort has gone into the first component mentioned above: data generation. In spite of the tremendous interest and activity, very few have taken the effort to make sure that the dataset used to train the ML-based PES is truly representative enough. Indeed data generation is often quite *ad hoc*. Some notable exceptions are found in[16], [17], [18]. In Ref.[17], an active learning procedure was proposed based on an existing unlabeled dataset. Some data points in that set are selected to be labeled, and the result is then used to train the ML model. The procedure ensures that the selected dataset is at least representative of the original unlabeled dataset. In Refs.[16], [18], one begins with no data, labeled or unlabeled, and explores the configuration spaces following some systematic procedure. For each of the configurations encountered, a decision is made as to whether that configuration should be labeled. The exploration procedure is designed to ensure that the unlabeled dataset, i.e.all the configurations encountered in the exploration, is representative of all the situations that the ML-based model is intended for. Even though these procedures were also described as “active learning”, there is a difference since in these procedures, one does not have the unlabeled data to begin with and choosing the unlabeled data judiciously is also an important part of the whole procedure.

To highlight this difference, we will call the procedures in Refs.[16], [18] “concurrent learning”. By “concurrent learning”, we mean that one does not have any data to begin with, labeled or unlabeled, and the data is generated on the fly as the training proceeds. The generation of the data and the learning is an interactive process to ensure that one obtains an “optimal” dataset which is on one hand representative enough and on the other hand as small as possible. This is in contrast to “sequential learning” in which the data is generated beforehand and the training of the model is performed afterwards. It also differs from active learning in the sense that active learning starts with unlabeled data. In a way the purpose of active learning is to find the smallest dataset that needs to be labeled in an existing unlabeled dataset.

The actual concurrent learning procedure goes as follows. One uses different sampling techniques (such as direct MD at different thermodynamic conditions, enhanced sampling, Monte Carlo) based on the current approximation of the PES to explore the configuration space. An efficient error indicator (this is the error in the PES) is then used to monitor the snapshots generated during the sampling process. Those that have significant errors will then be selected and sent to a labeling procedure, in which accurate *ab initio* energies and forces are calculated and added to the training dataset. A new approximation of the PES is obtained by training with the accumulated training dataset. These steps are repeated until convergence is achieved, i.e.,the configuration space has been explored sufficiently, and a representative set of data points has been accurately labeled. At the end of this procedure, a uniformly accurate PES model is generated. We refer to Ref.[18] for more details.

In order to carry out such a procedure efficiently, one also needs reasonable computational resources. Since many tasks can be done in parallel, one needs to implement automatic and efficient parallel processing algorithms. Taking the exploration stage for example, it may happen that dozens to hundreds of MD simulations are executed simultaneously with different initial configurations under different thermodynamic conditions. If these tasks are executed manually, it will require a great deal of human labor, not to mention the compromise in efficiency. It would be even worse if one wants to utilize different computational resources in different concurrent learning steps, e.g.,a high performance cluster (HPC) with most advanced GPU nodes for training and an HPC with a vast number of CPU nodes for labeling. Selecting a machine with the most available computational power among a group of candidate machines is also an issue. For all these reasons, we feel that it would be useful for the molecular and materials simulation community to have an open-source implementation of the concurrent learning procedure which, among other things, can automatically schedule the iterative process, dispatch different computational tasks to different computational resources, and collect and analyze the results.

In this paper, we introduce DP-GEN, an open-source concurrent learning platform and software package for the generation of reliable deep learning based PES models, in a way that minimizes the computational cost and human intervention. We describe the implementation of DP-GEN, which is based on the procedure proposed in Ref.[18]. We will focus on two modules, the scheduler and the task dispatcher. A modularized coding structure for the scheduler is designed, making it possible to incorporate different methods or software packages for the three different components in the concurrent learning procedure: exploration, labeling, and training. The dispatcher module is prepared for handling a huge number of tasks in a high-throughput fashion, and it is made compatible with different kinds of machines and popular job scheduling systems.

This paper is organized as follows. In Section2 we present the basic methodology that the DP-GEN workflow follows. In Section3 we introduce the details of the software, including how the concurrent learning process is scheduled and how different tasks are dispatched. In Section4, we give a concrete example, in which a general purpose Deep Potential model for Cu is generated using DP-GEN. Conclusions and outlooks are given in the last Section.

## Section snippets

## Methodology

The DP-GEN workflow contains a series of successive iterations. Each iteration is composed of three steps: exploration, labeling, and training. We denote by ${E}_{\omega}\left(\mathcal{R}\right)$, abbreviated ${E}_{\omega}$, the PES represented by the DP model, where $\mathcal{R}$ denotes atomic positions and $\omega $ denotes the parameters. An important point throughout the DP-GEN procedure is that we have an ensemble of models $\{{E}_{{\omega}_{1}},{E}_{{\omega}_{2}},\dots ,{E}_{{\omega}_{\alpha}},\dots \phantom{\rule{0ex}{0ex}}\}$ trained from the same set of data but with difference in the initialization of model parameters ${\omega}_{\alpha}$. ${\omega}_{\alpha}$ evolves

## Overview

Implemented with Python, DP-GEN provides a user-friendly interface. The master process can be started via a single line of command:

where the arguments PARAM and MACHINE are both the names of parameter files in the json format that specify the user’s demands.DP-GEN is composed of two major modules. First, DP-GEN serves as a scheduler, which follows the aforementioned concurrent learning scheme and generates computational tasks iteratively for the three steps: exploration, labeling and

## Examples

In this section, we report the details of the process that we follow to generate a DP model for Cu with DP-GEN, and demonstrate its uniform accuracy when used to predict a broad range of properties.

## Conclusion

In this paper, we introduced the software platform DP-GEN. We described its implementation and reported the details when used to generate a general purpose PES model for Cu. We expect DP-GEN to be a scalable and flexible platform. The three steps, exploration, training, and labeling, which are controlled by the scheduler, are separate and highly modularized. Therefore, developers will spend a minimal amount of effort to incorporate novel functionalities. For example, DP-GEN can easily be

## Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Acknowledgments

The authors thank Marcos F. Calegari Andrade, Hsin-Yu Ko, Jianxing Huang, Yunpei Liu, Mengchao Shi, Fengbo Yuan, and Yongbin Zhuang for helps and discussions. We are grateful for computing time provided by the TIGRESS High Performance Computer Center at Princeton University, the High-performance Computing Platform of Peking University, and the Beijing Institute of Big Data Research. The work of L. Z. and W. E was supported in part by a gift from iFlytek to Princeton University, USA, the ONR, USA

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