Progress of the ABACUS Software for Density Functional Theory and Its Integration and Applications with Deep Learning Algorithms
Received date: 2024-05-29
Revised date: 2024-07-28
Online published: 2024-09-05
Supported by
National Natural Science Foundation of China(12122401,12135002)
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).
CHEN Mohan . 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 . DOI: 10.11900/0412.1961.2024.00182
| 1 | Zhang L F, Han J Q, Wang H, et al. Deep potential molecular dynamics: A scalable model with the accuracy of quantum mechanics [J]. Phys. Rev. Lett., 2018, 120: 143001 |
| 2 | Wang H, Zhang L F, Han J Q, et al. DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics [J]. Comput. Phys. Commun., 2018, 228: 178 |
| 3 | Hohenberg P, Kohn W. Inhomogeneous electron gas [J]. Phys. Rev., 1964, 136: B864 |
| 4 | Kohn W, Sham L J. Self-consistent equations including exchange and correlation effects [J]. Phys. Rev., 1965, 140: A1133 |
| 5 | Kulik H J, Hammerschmidt T, Schmidt J, et al. Roadmap on machine learning in electronic structure [J]. Electron. Struct., 2022, 4: 023004 |
| 6 | Chen Y X, Zhang L F, Wang H, et al. DeePKS-kit: A package for developing machine learning-based chemically accurate energy and density functional models [J]. Comput. Phys. Commun., 2023, 282: 108520 |
| 7 | Kirkpatrick J, Mcmorrow B, Turban D H P, et al. Pushing the frontiers of density functionals by solving the fractional electron problem [J]. Science, 2021, 374: 1385 |
| 8 | Li H, Tang Z C, Fu J H, et al. Deep-learning density functional perturbation theory [J]. Phys. Rev. Lett., 2024, 132: 096401 |
| 9 | Li H, Wang Z, Zou N L, et al. Deep-learning density functional theory Hamiltonian for efficient ab initio electronic-structure calculation [J]. Nat. Comput. Sci., 2022, 2: 367 |
| 10 | Gong X X, Li H, Zou N L, et al. General framework for E(3)-equivariant neural network representation of density functional theory Hamiltonian [J]. Nat. Commun., 2023, 14: 2848 |
| 11 | Ellis J A, Fiedler L, Popoola G A, et al. Accelerating finite-temperature Kohn-Sham density functional theory with deep neural networks [J]. Phys. Rev., 2021, 104B: 035120 |
| 12 | Fiedler L, Moldabekov Z A, Shao X C, et al. Accelerating equilibration in first-principles molecular dynamics with orbital-free density functional theory [J]. Phys. Rev. Res., 2022, 4: 043033 |
| 13 | Gong W Y, Sun T, Bai H X, et al. Incorporation of density scaling constraint in density functional design via contrastive representation learning [J]. Digital Discovery, 2023, 2: 1404 |
| 14 | Kresse G, Furthmüller J. Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set [J]. Phys. Rev., 1996, 54B: 11169 |
| 15 | Kresse G, Furthmüller J. Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set [J]. Comput. Mater. Sci., 1996, 6: 15 |
| 16 | Giannozzi P, Baroni S, Bonini N, et al. Quantum espresso: A modular and open-source software project for quantum simulations of materials [J]. J. Phys. Condens. Matter, 2009, 21: 395502 |
| 17 | Hutter J, Iannuzzi M, Schiffmann F, et al. CP2K: Atomistic simulations of condensed matter systems [J]. WIREs Comput. Mol. Sci., 2014, 4: 15 |
| 18 | Soler J M, Artacho E, Gale J D, et al. The SIESTA method for ab initio order-N materials simulation [J]. J. Phys. Condens. Matter, 2002, 14: 2745 |
| 19 | Ozaki T. Variationally optimized atomic orbitals for large-scale electronic structures [J]. Phys. Rev., 2003, 67B: 155108 |
| 20 | Blum V, Gehrke R, Hanke F, et al. Ab initio molecular simulations with numeric atom-centered orbitals [J]. Comput. Phys. Commun., 2009, 180: 2175 |
| 21 | Hamann D R, Schlüter M, Chiang C. Norm-conserving pseudopotentials [J]. Phys. Rev. Lett., 1979, 43: 1494 |
| 22 | Perdew J P, Zunger A. Self-interaction correction to density-functional approximations for many-electron systems [J]. Phys. Rev., 1981, 23B: 5048 |
| 23 | Perdew J P, Burke K, Ernzerhof M. Generalized gradient approximation made simple [J]. Phys. Rev. Lett., 1996, 77: 3865 |
| 24 | Sun J W, Ruzsinszky A, Perdew J P. Strongly constrained and appropriately normed semilocal density functional [J]. Phys. Rev. Lett., 2015, 115: 036402 |
| 25 | Perdew J P, Ernzerhof M, Burke K. Rationale for mixing exact exchange with density functional approximations [J]. J. Chem. Phys., 1996, 105: 9982 |
| 26 | Heyd J, Scuseria G E, Ernzerhof M. Hybrid functionals based on a screened Coulomb potential [J]. J. Chem. Phys., 2003, 118: 8207 |
| 27 | Zhang Y Z, Wang H D, Chen W J, et al. DP-GEN: A concurrent learning platform for the generation of reliable deep learning based potential energy models [J]. Comput. Phys. Commun., 2020, 253: 107206 |
| 28 | Larsen A H, Mortensen J J, Blomqvist J, et al. The atomic simulation environment—A Python library for working with atoms [J]. J. Phys. Condens. Matter, 2017, 29: 273002 |
| 29 | Togo A, Chaput L, Tadano T, et al. Implementation strategies in phonopy and phono3py [J]. J. Phys. Condens. Matter, 2023, 35: 353001 |
| 30 | Mostofi A A, Yates J R, Lee Y S, et al. Wannier90: A tool for obtaining maximally-localised Wannier functions [J]. Comput. Phys. Commun., 2008, 178: 685 |
| 31 | Li W, Carrete J, Katcho N A, et al. ShengBTE: A solver of the Boltzmann transport equation for phonons [J]. Comput. Phys. Commun., 2014, 185: 1747 |
| 32 | Zheng Q J, Chu W B, Zhao C Y, et al. Ab initio nonadiabatic molecular dynamics investigations on the excited carriers in condensed matter systems [J]. WIREs Comput. Mol. Sci., 2019, 9: e1411 |
| 33 | Lin L, Chen M H, Yang C, et al. Accelerating atomic orbital-based electronic structure calculation via pole expansion and selected inversion [J]. J. Phys. Condens. Matter, 2013, 25: 295501 |
| 34 | Glass C W, Oganov A R, Hansen N. USPEX—Evolutionary crystal structure prediction [J]. Comput. Phys. Commun., 2006, 175: 713 |
| 35 | Lin P Z, Ren X G, He L X. Efficient hybrid density functional calculations for large periodic systems using numerical atomic orbitals [J]. J. Chem. Theory Comput., 2020, 17: 222 |
| 36 | Lin P Z, Ren X G, He L X. Accuracy of localized resolution of the identity in periodic hybrid functional calculations with numerical atomic orbitals [J]. J. Phys. Chem. Lett., 2020, 11: 3082 |
| 37 | Jin G, Pang H S, Ji Y Y, et al. PYATB: An efficient python package for electronic structure calculations using ab initio tight-binding model [J]. Comput. Phys. Commun., 2023, 291: 108844 |
| 38 | Chen M H, Ko H Y, Remsing R C, et al. Ab initio theory and modeling of water [J]. Proc. Natl. Acad. Sci. USA, 2017, 114: 10846 |
| 39 | Peng H W, Yang Z H, Perdew J P, et al. Versatile van der Waals density functional based on a meta-generalized gradient approximation [J]. Phys. Rev., 2016, 6X: 041005 |
| 40 | Payne M C, Teter M P, Allan D C, et al. Iterative minimization techniques for ab initio total-energy calculations: Molecular dynamics and conjugate gradients [J]. Rev. Mod. Phys., 1992, 64: 1045 |
| 41 | Bl?chl P E. Projector augmented-wave method [J]. Phys. Rev., 1994, 50B: 17953 |
| 42 | Vanderbilt D. Soft self-consistent pseudopotentials in a generalized eigenvalue formalism [J]. Phys. Rev., 1990, 41B: 7892(R) |
| 43 | Zhang D, Liu X Z J, Zhang X Y, et al. DPA-2: Towards a universal large atomic model for molecular and material simulation [DB/OL]. arXiv: 2312. 15492v1, 2023 |
| 44 | Lin P Z, Ren X G, Liu X H, et al. Ab initio electronic structure calculations based on numerical atomic orbitals: Basic fomalisms and recent progresses [J]. WIREs Comput. Mol. Sci., 2024, 14: e1687 |
| 45 | Junquera J, Paz ó, Sánchez-Portal D, et al. Numerical atomic orbitals for linear-scaling calculations [J]. Phys. Rev., 2001, 64B: 235111 |
| 46 | Chen M H, Guo G C, He L X. Systematically improvable optimized atomic basis sets for ab initio calculations [J]. J. Phys. Condens. Matter, 2010, 22: 445501 |
| 47 | Chen M H, Guo G C, He L X. Electronic structure interpolation via atomic orbitals [J]. J. Phys. Condens. Matter, 2011, 23: 325501 |
| 48 | Li P F, Liu X H, Chen M H, et al. Large-scale ab initio simulations based on systematically improvable atomic basis [J]. Comput. Mater. Sci., 2016, 112: 503 |
| 49 | Zheng D Y, Ren X G, He L X. Accurate stress calculations based on numerical atomic orbital bases: Implementation and benchmarks [J]. Comput. Phys. Commun., 2021, 267: 108043 |
| 50 | Lin P Z, Ren X G, He L X. Strategy for constructing compact numerical atomic orbital basis sets by incorporating the gradients of reference wavefunctions [J]. Phys. Rev., 2021, 103B: 235131 |
| 51 | Liu Y, Ding X L, Chen M H, et al. A caveat of the charge-extrapolation scheme for modeling electrochemical reactions on semiconductor surfaces: An issue induced by a discontinuous Fermi level change [J]. Phys. Chem. Chem. Phys., 2022, 24: 15511 |
| 52 | Zheng Y, Liu F, Nan H Q, et al. Disordered hyperuniformity in two-dimensional amorphous silica [J]. Sci. Adv., 2020, 6: eaba0826 |
| 53 | Chen D Y, Liu Y, Zheng Y, et al. Disordered hyperuniform quasi-one-dimensional materials [J]. Phys. Rev., 2022, 106B: 235427 |
| 54 | Jin G, He L X. Peculiar band geometry induced giant shift current in ferroelectric SnTe monolayer [J]. npj Comput. Mater., 2024, 10: 23 |
| 55 | Gillen R, Maultzsch J. Family behavior and Dirac bands in armchair nanoribbons with 4-8 defect lines [J]. J. Phys. Condens. Matter., 2024, 36: 295501 |
| 56 | Pang H S, Jin G, He L X. Tuning of Berry-curvature dipole in Ta-As slabs: An effective route to enhance the nonlinear Hall response [J]. Phys. Rev. Mater., 2024, 8: 043403 |
| 57 | Zhao Z Z, Sun M L, Ji Y Y, et al. Efficient homojunction tin perovskite solar cells enabled by gradient germanium doping [J]. Nano Lett., 2024, 25: 5513 |
| 58 | Dai Z J, Jin G, He L X. Interplay between magnetic structures and surface states in MnBi2Te4 from first-principles studies [J]. Phys. Rev., 2023, 108B: 085112 |
| 59 | Zheng D Y, Shen Z X, Chen M H, et al. Retention and recycling of deuterium in liquid lithium-tin slab studied by first-principles molecular dynamics [J]. J. Nucl. Mater., 2021, 543: 152542 |
| 60 | Liu X H, Zheng D Y, Ren X G, et al. First-principles molecular dynamics study of deuterium diffusion in liquid tin [J]. J. Chem. Phys., 2017, 147: 064505 |
| 61 | Wang Q P, Zheng D Y, He L X, et al. Cooperative effect in a graphite intercalation compound: Enhanced mobility of AlCl4 in the graphite cathode of aluminum-ion batteries [J]. Phys. Rev. Appl., 2019, 12: 044060 |
| 62 | Liu Y, Ren X G, He L X. A DFT study of energetic and structural properties of a full turn of A-form DNA under relaxed and stretching conditions [J]. J. Chem. Phys., 2019, 151: 215102 |
| 63 | Liu Y, Liu X H, Chen M H. Copper-doped beryllium and beryllium oxide interface: A first-principles study [J]. J. Nucl. Mater., 2021, 545: 152733 |
| 64 | Shao X C, Qu X, Liu S Y, et al. Structure evolution of chromium-doped boron clusters: Toward the formation of endohedral boron cages [J]. RSC Adv., 2019, 9: 2870 |
| 65 | Liu X H, Qi Y H, Zheng D Y, et al. Diffusion coefficients of Mg isotopes in MgSiO3 and Mg2SiO4 melts calculated by first-principles molecular dynamics simulations [J]. Geochim. Cosmochim. Acta, 2018, 223: 364 |
| 66 | Baer R, Neuhauser D, Rabani E. Self-averaging stochastic Kohn-Sham density-functional theory [J]. Phys. Rev. Lett., 2013, 111: 106402 |
| 67 | White A J, Collins L A. Fast and universal Kohn-Sham density functional theory algorithm for warm dense matter to hot dense plasma [J]. Phys. Rev. Lett., 2020, 125: 055002 |
| 68 | Liu Q R, Chen M H. Plane-wave-based stochastic-deterministic density functional theory for extended systems [J]. Phys. Rev., 2022, 106B: 125132 |
| 69 | Chen T, Liu Q R, Liu Y, et al. Combining stochastic density functional theory with deep potential molecular dynamics to study warm dense matter [J]. Matter Radiat. Extremes, 2024, 9: 015604 |
| 70 | Chen Y X, Zhang L F, Wang H, et al. DeePKS: A comprehensive data-driven approach toward chemically accurate density functional theory [J]. J. Chem. Theory Comput., 2021, 17: 170 |
| 71 | Li W F, Ou Q, Chen Y X, et al. DeePKS + ABACUS as a bridge between expensive quantum mechanical models and machine learning potentials [J]. J. Phys. Chem., 2022, 126A: 9154 |
| 72 | Ou Q, Tuo P, Li W F, et al. DeePKS model for halide perovskites with the accuracy of a hybrid functional [J]. J. Phys. Chem., 2023, 127C: 18755 |
| 73 | Zhang P C, Gardini A T, Xu X F, et al. Intramolecular and water mediated tautomerism of solvated glycine [J]. J. Chem. Inf. Model., 2024, 64: 3599 |
| 74 | Zhang L F, Lin D Y, Wang H, et al. Active learning of uniformly accurate interatomic potentials for materials simulation [J]. Phys. Rev. Mater., 2019, 3: 023804 |
| 75 | Zhang D, Bi H R, Dai D Z, et al. Pretraining of attention-based deep learning potential model for molecular simulation [J]. npj Comput. Mater., 2024, 10: 94 |
| 76 | Wu J, Yang J Y, Liu Y J S, et al. Universal interatomic potential for perovskite oxides [J]. Phys. Rev., 2023, 108B: L180104 |
| 77 | Liu J C, Zhang X C, Chen T, et al. Machine-learning-based interatomic potentials for group IIB to VIA semiconductors: Toward a universal model [J]. J. Chem. Theory Comput., 2024, 20: 5717 |
| 78 | Wang Y A, Govind N, Carter E A. Orbital-free kinetic-energy functionals for the nearly free electron gas [J]. Phys. Rev., 1998, 58B: 13465 |
| 79 | Wang L W, Teter M P. Kinetic-energy functional of the electron density [J]. Phys. Rev., 1992, 45: 13196 |
| 80 | Chen M H, Jiang X W, Zhuang H L, et al. Petascale orbital-free density functional theory enabled by small-box algorithms [J]. J. Chem. Theory Comput., 2016, 12: 2950 |
| 81 | Wang Y A, Govind N, Carter E A. Orbital-free kinetic-energy density functionals with a density-dependent kernel [J]. Phys. Rev., 1999, 60B: 16350 |
| 82 | Huang C, Carter E A. Nonlocal orbital-free kinetic energy density functional for semiconductors [J]. Phys. Rev., 2010, 81B: 045206 |
| 83 | Huang C, Carter E A. Transferable local pseudopotentials for magnesium, aluminum and silicon [J]. Phys. Chem. Chem. Phys., 2008, 10: 7109 |
| 84 | Sun L, Chen M H. Machine learning based nonlocal kinetic energy density functional for simple metals and alloys [J]. Phys. Rev., 2024, 109B: 115135 |
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