PyGAMD: Python graphics processing unit-accelerated molecular dynamics software

Abstract

PyGAMD (Python GPU-accelerated molecular dynamics software) is a molecular simulation platform developed from scratch. It is designed for soft matter, especially for polymer by integrating coarse-grained/multi-scale models, methods, and force fields. It essentially includes an interpreter of molecular dynamics (MD) which supports secondary programming so that users can write their own functions by themselves, such as analytical potential forms for nonbonded, bond, angle, and dihedral interactions in an easy way, greatly extending the flexibility of MD simulations. The interpreter is written by pure Python language, making it easy to be modified and further developed. Some built-in libraries written by other languages that have been compiled for Python are added into PyGAMD to extend it's features, including configuration initialization, property analysis, etc. Machine learning force fields that are trained by DeePMD-kit are supported by PyGAMD for conveniently implementing multi-scale modeling and simulations. By designing an advanced framework of software, graphics processing unit-acceleration achieved by the Numba library of Python and compute unified device architecture reaches a high computing efficiency.

Keywords

GPU-acceleration / interpreter / molecular dynamics / multi-scale / polymer / soft matter

References

[1] Su YJ, Fu HD, Bai Y, Jiang X, Xie JX. Progress in materials genome engineering in China. Acta Metall Sin. 2020; 56(10): 1313-1323.

[2] Xie JX. Prospects of materials genome engineering frontiers. MGE Adv. 2023; 1(2):e17.

[3] Zhong M, Tran K, Min YM, et al. Accelerated discovery of CO₂ electrocatalysts using active machine learning. Nature. 2020; 581(7807): 178-183.

[4] Xu YF, Elcoro L, Song ZD, et al. High-throughput calculations of magnetic topological materials. Nature. 2020; 586(7831): 702-707.

[5] Yan WL, Mu ZL, Wang ZX, et al. Hard-carbon-stabilized Li-Si anodes for high-performance all-solid-state Li-ion batteries. Nat Energy. 2023; 8(8): 800-813.

[6] Choudhary K, Garrity KF, Reid ACE, et al. The joint automated repository for various integrated simulations (JARVIS) for data-driven materials design. npj Comput Mater. 2020; 6(1): 1-13.

[7] Xu DG, Zhang Q, Huo XY, Wang YT, Yang ML. Advances in data-assisted high-throughput computations for material design. MGE Adv. 2023; 1(1):e11.

[8] Qiu HK, Sun ZY. On-demand reverse design of polymers with PolyTAO. npj Comput Mater. 2024; 10(1): 273.

[9] Gao L, Lin JP, Wang LQ, Du L. Machine learning-assisted design of advanced polymeric materials. Acc Mater Res. 2024; 5(5): 571-584.

[10] Pablo dJJ, Jones B, Kovacs CL, Ozolins V, Ramirez AP. The Materials Genome Initiative, the interplay of experiment, theory and computation. Curr Opin Solid State Mater Sci. 2014; 18(2): 99-117.

[11] Kremer K, Müller-Plathe F. Multiscale simulation in polymer science. Mol Simul. 2002; 28(8-9): 729-750.

[12] Chen LJ, Qian HJ, Lu ZY, Li ZS, Sun CC. An automatic coarse-graining and fine-graining simulation method: application on polyethylene. J Phys Chem B. 2006; 110(47): 24093-24100.

[13] Zeng QH, Yu AB, Lu GQ. Multiscale modeling and simulation of polymer nanocomposites. Prog Polym Sci. 2008; 33(2): 191-269.

[14] Yang H, Wu S, Chen Q. Toughening vitrimers based on dioxaborolane metathesis through introducing a reversible secondary interaction. Poly Sci Technol. 2024.

[15] Reith D, Pütz M, Müller-Plathe F. Deriving effective mesoscale potentials from atomistic simulations. J Comput Chem. 2003; 24(13): 1624-1636.

[16] Karimi-Varzaneh HA, Qian HJ, Chen X, Carbone P, Müller-Plathe F. IBIsCO: a molecular dynamics simulation package for coarse-grained simulation. J Comput Chem. 2011; 32(7): 1475-1487.

[17] Rühle V, Junghans C, Lukyanov A, Kremer K, Andrienko D. Versatile object-oriented toolkit for coarse-graining applications. J Chem Theor Comput. 2009; 5(12): 3211-3223.

[18] Milano G, Kawakatsu T. Hybrid particle-field molecular dynamics simulations for dense polymer systems. J Chem Phys. 2009; 130(21):214106.

[19] De Nicola A, Zhao Y, Kawakatsu T, Roccatano D, Milano G. Hybrid particle-field coarse-grained models for biological phospholipids. J Chem Theor Comput. 2011; 7(9): 2947-2962.

[20] Zhao Y, De Nicola A, Kawakatsu T, Milano G. Hybrid particle-field molecular dynamics simulations: parallelization and benchmarks. J Comput Chem. 2012; 33(8): 868-880.

[21] Mitsutake A, Sugita Y, Okamoto Y. Generalized-ensemble algorithms for molecular simulations of biopolymers. Pept Sci. 2001; 60(2): 96-123.

[22] Plimpton S. Fast parallel algorithms for short-range molecular dynamics. J Comput Phys. 1995; 117(1): 1-19.

[23] Thompson AP, Aktulga HM, Berger R, et al. LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales. Comput Phys Commun. 2022; 271:108171.

[24] Eastman P, Swails J, Chodera JD, et al. OpenMM 7: rapid development of high performance algorithms for molecular dynamics. PLoS Comput Biol. 2017; 13(7): 1-17.

[25] Refson K. Moldy: a portable molecular dynamics simulation program for serial and parallel computers. Comput Phys Commun. 2000; 126(3): 310-329.

[26] Yu ZC, Pu X, Han BY, Zhu YL, Lu ZY. Polymer molecular dynamics simulation software PyGAMD and its adaptation to the domestic heterogeneous computing platform. Front Data Comput. 2024; 6(1): 3-11.

[27] Eastman P, Friedrichs MS, Chodera JD, et al. OpenMM 4: a reusable, extensible, hardware independent library for high performance molecular simulation. J Chem Theor Comput. 2013; 9(1): 461-469.

[28] Zhu YL, Liu H, Li ZW, Qian HJ, Milano G, Lu ZY. GALAMOST: GPU-accelerated large-scale molecular simulation toolkit. J Comput Chem. 2013; 34(25): 2197-2211.

[29] Zhu YL, Pan D, Li ZW, et al. Employing multi-GPU power for molecular dynamics simulation: an extension of GALAMOST. Mol Phys. 2018; 116(7-8): 1065-1077.

[30] Wang H, Zhang LF, Han JQ, E WN. DeePMD-kit: a deep learning package for many-body potential energy representation and molecular dynamics. Comput Phys Commun. 2018; 228: 178-184.

[31] Zhang LF, Han JQ, Wang H, Car R, E WN. Deep potential molecular dynamics: a scalable model with the accuracy of quantum mechanics. Phys Rev Lett. 2018; 120(14):143001.

[32] Zeng JZ, Zhang D, Lu DH, et al. DeePMD-kit v2: a software package for deep potential models. J Chem Phys. 2023; 159(5):054801.

[33] Hoogerbrugge P, Koelman J. Simulating microscopic hydrodynamic phenomena with dissipative particle dynamics. Europhys Lett. 1992; 19(3): 155-160.

[34] Groot RD, Warren PB. Dissipative particle dynamics: bridging the gap between atomistic and mesoscopic simulation. J Chem Phys. 1997; 107(11): 4423-4435.

[35] Groot RD, Madden TJ. Dynamic simulation of diblock copolymer microphase separation. J Chem Phys. 1998; 108(20): 8713-8724.

[36] Li XJ, Guo JY, Liu Y, Liang HJ. Microphase separation of diblock copolymer poly(styrene-b-isoprene): a dissipative particle dynamics simulation study. J Chem Phys. 2009; 130(7):074908.

[37] Kmiecik S, Gront D, Kolinski M, Wieteska L, Dawid AE, Kolinski A. Coarse-grained protein models and their applications. Chem Rev. 2016; 116(14): 7898-7936.

[38] Prestipino S, Saija F, Giaquinta PV. Phase diagram of the Gaussian-core model. Phys Rev E. 2005; 71(5):050102.

[39] Poplavskyy O, Goerbig MO, Morais Smith C. Local density of states of electron-crystal phases in graphene in the quantum Hall regime. Phys Rev B. 2009; 80(19):195414.

[40] Montes-Saralegui M, Nikoubashman A, Kahl G. Hopping and diffusion of ultrasoft particles in cluster crystals in the explicit presence of a solvent. J Phys Condens Matter. 2013; 25(19):195101.

[41] Diaz-Mendez R, Mezzacapo F, Cinti F, Lechner W, Pupillo G. Monodisperse cluster crystals: classical and quantum dynamics. Phys Rev E. 2015; 92(5):052307.

[42] Zhao C, Sun GK, Lei BC, Zhang LL, Sun ZY, Zhu YL. Crystallization behaviors of polydisperse ultra-soft spherical system. Chem J Chin Univ. 2024; 45(12):20240388.

[43] Su X, Xu XP, Ji ZQ, Wu J, Ma F, Fan LZ. Polyethylene oxide-based composite solid electrolytes for lithium batteries: current progress, low-temperature and high-voltage limitations, and prospects. Electrochem Energy Rev. 2024; 7(1): 2.

[44] Guo Y, Wang M, Liu Q, Liu GL, Wang S, Li JN. Recent advances in the medical applications of hemostatic materials. Theranostics. 2023; 13(1): 161-196.

[45] Abraham MJ, Murtola T, Schulz R, et al. GROMACS: high performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX. 2015; 1–2: 19-25.

[46] Iftimie R, Minary P, Tuckerman ME. Ab initio molecular dynamics: concepts, recent developments, and future trends. Proc Natl Acad Sci USA. 2005; 102(19): 6654-6659.

[47] Unke OT, Chmiela S, Sauceda HE, et al. Machine learning force fields. Chem Rev. 2021; 121(16): 10142-10186.

[48] Laasonen K, Sprik M, Parrinello M, Car R. “Ab initio” liquid water. J Chem Phys. 1993; 99(11): 9080-9089.

[49] Soper A, Phillips M. A new determination of the structure of water at 25°C. Chem Phys. 1986; 107(1): 47-60.

[50] Zhu YL, Lu ZY, Sun ZY. The mechanism of the emergence of distinct overstretched DNA states. J Chem Phys. 2016; 144(2):024901.

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2025 The Author(s). Materials Genome Engineering Advances published by Wiley-VCH GmbH on behalf of University of Science and Technology Beijing.