密度泛函理论软件<strong>ABACUS</strong>进展及其与深度学习算法的融合及应用

doi:10.11900/0412.1961.2024.00182

金属学报

2024, Vol. 60

Issue (10): 1405-1417 DOI: 10.11900/0412.1961.2024.00182

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密度泛函理论软件ABACUS进展及其与深度学习算法的融合及应用

陈默涵¹^,²(

)

1 北京大学工学院应用物理与技术研究中心高能量密度物理数值模拟教育部重点实验室北京 100871
2 北京科学智能研究院北京 100080

Progress of the ABACUS Software for Density Functional Theory and Its Integration and Applications with Deep Learning Algorithms

CHEN Mohan¹^,²(

)

1 HEDPS, CAPT, College of Engineering, Peking University, Beijing 100871, China
2 AI for Science Institute, Beijing 100080, China

引用本文:

陈默涵. 密度泛函理论软件ABACUS进展及其与深度学习算法的融合及应用[J]. 金属学报, 2024, 60(10): 1405-1417.
Mohan CHEN. Progress of the ABACUS Software for Density Functional Theory and Its Integration and Applications with Deep Learning Algorithms[J]. Acta Metall Sin, 2024, 60(10): 1405-1417.

全文: PDF(2076 KB) HTML

摘要:

基于量子力学基本原理的密度泛函理论(DFT)可以有效预测材料性质，如今它在物理、化学、材料、生物等领域的研究工作中已得到广泛应用。随着对材料领域的深入研究，进一步提升DFT的精度和效率已成为迫切需求，但精度和效率往往不可兼得。近年来，在AI for Science理念的引领下，基于深度学习的电子结构计算方法迅速发展，有望解决电子结构计算中精度和效率不能两全的困境。然而，只有稳定可靠的DFT软件平台才能保证深入研究和持续推动AI辅助电子结构计算方法的广泛应用，这既充满挑战又蕴含机遇。在此背景下，本文主要从物理模型、深度学习算法和软件开发3方面介绍国产开源DFT软件ABACUS (atomic-orbital based ab-initio computation at UStc，中文名原子算筹)从开发2.2版本(2022年4月发布)到发布3.7版本(2024年7月发布)期间的进展及其与深度学习算法的融合和应用。

关键词 ： ABACUS, 密度泛函理论, 开源软件, 深度学习, 计算材料科学

Abstract：

Density functional theory (DFT), grounded in the fundamental principles of quantum mechanics, effectively predicts material properties and is now widely used across various research disciplines such as physics, chemistry, materials science, and biology. As research in materials science advances, there is an urgent need to further enhance the accuracy and efficiency of DFT. However, improving accuracy and efficiency is often challenging because these goals can be mutually exclusive. Recently, guided by the concept of AI for science, deep learning-based electronic structure calculation methods have rapidly emerged, offering potential solutions to resolve this accuracy-efficiency dilemma. Nonetheless, developing a stable and reliable DFT software platform remains a substantial challenge in exploring and expanding the use of AI-assisted methods on a broader scale. This paper introduces the open-source DFT package ABACUS (atomic-orbital based ab-initio computation at UStc), focusing on its physical models, deep learning algorithms, and software development aspects. The present discussion emphasizes the progress of the open-source package, highlighting its integration with deep learning algorithms and its evolution from version 2.2 (released in April 2022) to version 3.7 (released in July 2024).

Key words： ABACUS density functional theory open-source software deep learning computational materials science

收稿日期: 2024-05-29

ZTFLH:

O469

基金资助:国家自然科学基金项目(12122401,12135002)

通讯作者: 陈默涵，mohanchen@pku.edu.cn，主要从事计算物理研究

Corresponding author: CHEN Mohan, Tel: (010)62757475, E-mail: mohanchen@pku.edu.cn

作者简介: 陈默涵，男，1985年出生，博士

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https://www.ams.org.cn/CN/10.11900/0412.1961.2024.00182 或 https://www.ams.org.cn/CN/Y2024/V60/I10/1405

图1 实现于原子算筹(ABACUS)中的DeePKS方法可学习到高阶泛函的精度。此外，该方法还具有较高计算效率

图2 采用ABACUS训练的半导体势函数大模型DP-Semi，可用于模拟半导体的熔点等性质[77]

图3 基于深度学习的电子动能密度泛函发展及其在合金中的应用[84]

Date of releasing ABACUS	Version	Content
2022.04.08	v2.2	Version 2.2 went through 3,200 commit modifications on GitHub from version 2.1, and the development team includes developers from various domestic research institutions
2022.07.01	v2.3	Version 2.3 incorporates new plane wave generation and parallel modules, enhancing support for parallel computing with plane wave basis sets; the functionalities of the stochastic wave function density functional theory method have been released
2022.10.01	v3.0	Version 3.0 has been released with the deep learning-based DeePKS functional approach based on periodic boundary conditions. Interfaces with the machine learning potential function method DeePMD-kit and the DP-GEN method have been released, as well as interfaces with the DeepH method for constructing Hamiltonians of electronic systems through machine learning
2023.01.01	v3.1	Version 3.1 has been released with support for GPU and domestic DCU hardware in plane-wave basis set solutions for the Kohn-Sham equations, and introduces new features for the computation of solid-liquid interfaces
2023.04.01	v3.2	Version 3.2 has been released with an interface for Hefei-NAMD software. The coverage of unit tests has increased to 60%, and the total coverage is now 76%
2023.07.13	v3.3	Version 3.3 introduces an interface for calculating hybrid functionals with LibRI, and an interface with ShengBTE software for conducting material thermal conductivity calculations
2023.10.07	v3.4	Version 3.4 primarily supports the DPA method, a machine learning potential function for universal large models, and has reconstructed core algorithms such as more efficient and reliable numerical atomic orbital two-center integrals
2023.12.29	v3.5	Version 3.5 improved the algorithm for charge mixing and supported ultrasoft pseudopotentials for plane-wave basis sets, with targeted code development and optimization for three practical application scenarios: semiconductors, alloys, and batteries
2024.04.01	v3.6	Version 3.6 has released a more stable and reliable DFT + U algorithm function and further optimized the program's computational performance on Sunway DCU hardware
2024.07.01	v3.7	Version 3.7 has further supported the OpenLAM Large Atom Model project on a larger scale

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