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[Tensorial properties via the neuroevolution potential framework: Fast simulation of infrared and Raman spectra](https://doi.org/10.1021/acs.jctc.3c01343),
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J. Chem. Theory Comput. **20**, 3273 (2024).
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[Song2025] Keke Song, Jiahui Liu, Shunda Chen, Zheyong Fan, Yanjing Su, Ping Qian, [Solute segregation in polycrystalline aluminum from hybrid Monte Carlo and molecular dynamics simulations with a unified neuroevolution potential](https://arxiv.org/abs/2404.13694),
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arXiv:2404.13694 [cond-mat.mtrl-sci]
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[Song2026] Keke Song, Jiahui Liu, Yuanxu Zhu, Shunda Chen, Zheyong Fan, Yanjing Su, Ping Qian, [Solute Segregation in Polycrystalline Aluminum From Hybrid Monte Carlo and Molecular Dynamics Simulations With a Unified Neuroevolution Potential](https://onlinelibrary.wiley.com/doi/10.1002/mgea.70049),
[Ying2025] Penghua Ying, Wenjiang Zhou, Lucas Svensson, Esmée Berger, Erik Fransson, Fredrik Eriksson, Ke Xu, Ting Liang, Jianbin Xu, Bai Song, Shunda Chen, Paul Erhart, Zheyong Fan, [Highly efficient path-integral molecular dynamics simulations with GPUMD using neuroevolution potentials: Case studies on thermal properties of materials](https://doi.org/10.1063/5.0241006),
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J. Chem. Phys. **162**, 064109 (2025).
@@ -139,10 +141,13 @@ Phys. Rev. B **110**, 224101 (2024).
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[Jiang2025] Wenwu Jiang, Ting Liang, Hekai Bu, Jianbin Xu, and Wengen Ouyang, [Moiré-driven interfacial thermal transport in twisted transition metal dichalcogenides](https://doi.org/10.1021/acsnano.4c12148),
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ACS Nano **19**, 16287 (2025).
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[Bu2025] Hekai Bu, Wenwu Jiang, Penghua Ying, Ting Liang, Zheyong Fan, and Wengen Ouyang, [Accurate modeling of LEGO-like vdW heterostructures: Integrating machine learned with anisotropic interlayer potentials](https://arxiv.org/abs/2504.12985),
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arXiv:2504.12985 [physics.comp-ph].
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[Liang2025] Ting Liang, Ke Xu, Eric Lindgren, Zherui Chen, Rui Zhao, Jiahui Liu, Esmée Berger, Benrui Tang, Bohan Zhang, Yanzhou Wang, Keke Song, Penghua Ying, Nan Xu, Haikuan Dong, Shunda Chen, Paul Erhart, Zheyong Fan, Tapio Ala-Nissila, Jianbin Xu, [NEP89: Universal neuroevolution potential for inorganic and organic materials across 89 elements](https://arxiv.org/abs/2504.21286), arXiv:2504.21286 [cond-mat.mtrl-sci].
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[Huang2026] Hongfu Huang, Junhao Peng, Kaiqi Li, Jian Zhou, Zhimei Sun, [Efficient GPU-accelerated training of a neuroevolution potential with analytical gradients](https://doi.org/10.1016/j.cpc.2025.109994),
[Fan2026a] Zheyong Fan, Benrui Tang, Esmée Berger, Ethan Berger, Erik Fransson, Ke Xu, Zihan Yan, Zhoulin Liu, Zichen Song, Haikuan Dong, Shunda Chen, Lei Li, Ziliang Wang, Yizhou Zhu, Julia Wiktor, Paul Erhart [qNEP: A highly efficient neuroevolution potential with dynamic charges for large-scale atomistic simulations](https://arxiv.org/abs/2601.19034), arXiv:2601.19034 [physics.comp-ph].
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[Fan2026b] Zheyong Fan, Wenjun Zhang, Zhenhao Zhang, Ke Xu, Xuecheng Shao, Haikuan Dong, [NEP-CG and NEP-AACG: Efficient coarse-grained and multiscale all-atom-coarse-grained neuroevolution potentials](https://arxiv.org/abs/2603.01234), arXiv:2603.01234 [physics.comp-ph] (2026).
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[Bu2026] Hekai Bu, Wenwu Jiang, Penghua Ying, Ting Liang, Zheyong Fan, and Wengen Ouyang, [Modular hybrid machine learning and physics-based potentials for scalable modeling of Van der Waals heterostructures](https://doi.org/10.1016/j.jmps.2026.106540), J. Mech. Phys. Solids **210**, 106540 (2026).
1. Starts `GPUMD` as MDI ENGINE in the background.
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2. Starts the Python VASP DRIVER (`vasp_mdi_driver.py`) as MDI DRIVER.
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3. Runs VASP at each MD step to compute QM energies and forces.
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4. Sends QM forces and energy back to `GPUMD` via MDI.
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5. Lets `GPUMD` perform the MD time integration.
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Standard `GPUMD` dump and compute keywords in`run.in` (for example, `dump_force`, `dump_thermo`, etc.) work as usual, so forces and thermodynamic quantities can be written to the standard output files while using QM forces from VASP.
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---
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## 6. Extending to other DRIVER codes
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The current example focuses on `VASP` as the QM DRIVER.
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In principle, any external code that implements the MDI protocol can be used as a DRIVER:
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- Replace `vasp_mdi_driver.py` with a DRIVER that:
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- connects to `GPUMD` over MDI,
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- requests coordinates from `GPUMD`,
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- computes energies and forces,
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- sends them back via the appropriate MDI commands.
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The MDI-specific logic inside `GPUMD` is generic and not tied to VASP.
Copy file name to clipboardExpand all lines: doc/gpumd/input_parameters/compute_cohesive.rst
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This keyword is used as follows::
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compute_cohesive <e1> <e2> <num_points>
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compute_cohesive <e1> <e2> <direction>
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Here,
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:attr:`e1` is the smaller box-scaling factor,
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:attr:`e2` is the larger box-scaling factor, and
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:attr:`num_points` is the number of points sampled uniformly from :attr:`e1` to :attr:`e2`.
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:attr:`direction` specifies the direction of the scaling, which can take values from 0 to 6, corresponding to the x, y, z, xy, yz, zx, and xyz directions, respectively.
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Examples
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--------
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The command::
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compute_cohesive 0.9 1.2 301
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compute_cohesive 0.9 1.2 0
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means that one wants to compute the cohesive energy curve from the box-scaling factor 0.9 to the box-scaling factor 1.2, with 301 points.
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means that one wants to compute the cohesive energy curve in the x-direction from the box-scaling factor 0.9 to the box-scaling factor 1.2, with ``(e2 - e1)*1000 +1`` points.
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The box-scaling points will be 0.9, 0.901, 0.902, ..., 1.2.
This means that the :term:`RDF` calculations will be performed every :attr:`interval` steps, with :attr:`num_bins` data points evenly distributed from 0 to :attr:`cutoff` (in units of Ångstrom) in terms of the distance between atom pairs.
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Without the optional parameters, only the total :term:`RDF` will be calculated.
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To additionally calculate the partial :term:`RDF` for a pair of species, one can specify the types of the two species after the word "atom".
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The types 0, 1, 2, ... correspond to the species in the potential file in order.
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Currently, one can specify at most 6 pairs.
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Starting from GPUMD-v4.9, there is no need to specify the atom pairs and the code will calculate the partial :term:`RDF`\s for all atom pairs.
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Example
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-------
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compute_rdf 8.0 400 1000 # total RDF every 1000 MD steps with 400 data up to 8 Angstrom
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compute_rdf 8.0 400 1000 atom 0 0 atom 1 1 atom 0 1 # additionally calculate 3 partial RDFs
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compute_rdf 8.0 400 1000 # Calculate all RDFs every 1000 MD steps with 400 data up to 8 Angstrom
Copy file name to clipboardExpand all lines: doc/nep/input_parameters/cutoff.rst
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cutoff <radial_cutoff> <angular_cutoff>
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where :attr:`<radial_cutoff>` and :attr:`<angular_cutoff>` correspond to :math:`r_\mathrm{c}^\mathrm{R}` and :math:`r_\mathrm{c}^\mathrm{A}`, respectively.
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The cutoffs must satisfy the conditions 2.5 Å :math:`\leq r_\mathrm{c}^\mathrm{A} \leq r_\mathrm{c}^\mathrm{R} \leq` 10 Å.
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The cutoffs must satisfy the conditions 3 Å :math:`\leq r_\mathrm{c}^\mathrm{A} \leq r_\mathrm{c}^\mathrm{R} \leq` 10 Å.
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The defaults are :math:`r_\mathrm{c}^\mathrm{R}` = 8 Å and :math:`r_\mathrm{c}^\mathrm{A}` = 4 Å.
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It can be computationally beneficial to use (possibly much) smaller :math:`r_\mathrm{c}^\mathrm{R}` but the default values should be reasonable in most cases.
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