Small amounts of heterogeneous data significantly impact the prediction results for lithium metal systems.

[ikuki-ikuki]

Hello everyone, I'm encountering perplexing issues while using DeepMD-kit for my research and would appreciate your insights. My study aims to simulate lithium dendrite growth in batteries by stacking lithium atoms onto a ternary layered material (effectively making it a quaternary system). I began by generating 20,000 initial data points through VASP calculations on bulk and slab models of lithium's three polymorphs (BCC, HCP, FCC), applying various perturbations. A potential trained solely on this lithium dataset accurately described metallic lithium behavior and successfully captured dendrite formation. However, when I augmented the training set with just 960 additional configurations of the substrate material (bulk structures), the potential catastrophically failed to describe lithium metal - showing drastically increased energy/force errors and exhibiting unphysical thermal motion in LAMMPS simulations.（Fig 1.) What underlying mechanisms could cause this degradation?
Attaching the dp test results, dp train script content.

`{

"_comment": "that's all",
"model": {
    "type_map": [
        "Ti",
        "In",
        "B",
        "Li"
    ],
    "descriptor": {
        "type": "se_e2_a",
        "sel": "auto",
        "rcut_smth": 0.5,
        "rcut": 6.0,
        "neuron": [
            25,
            50,
            100
        ],
        "resnet_dt": false,
        "axis_neuron": 16,
        "seed": 1,
        "_comment": "that's all"
    },
    "fitting_net": {
        "neuron": [
            240,
            240,
            240
        ],
        "resnet_dt": true,
        "seed": 1,
        "_comment": "that's all"
    },
    "_comment": "that's all"
},
"learning_rate": {
    "type": "exp",
    "decay_steps": 2500,
    "start_lr": 0.001,
    "stop_lr": 3.51e-08,
    "_comment": "that's all"
},
"loss": {
    "type": "ener",
    "start_pref_e": 0.02,
    "limit_pref_e": 1,
    "start_pref_f": 1000,
    "limit_pref_f": 1,
    "start_pref_v": 0,
    "limit_pref_v": 0,
    "_comment": "that's all"
},
"training": {
    "training_data": {
        "systems": [
            "..../dp/data/BULK/700/train",
"..../data/Li/BCC/NVT/350/train",
"..../data/Li/BCC/NVT/400/train",
    ......
        ],
        "batch_size": "auto",
        "_comment": "that's all"
    },
    "validation_data": {
        "systems": [
            "...../data/BULK/700/val",
"..../data/Li/BCC/NVT/350/val",
"..../data/Li/BCC/NVT/400/val",
     ......
        ],
        "batch_size": "auto",
        "numb_btch": 1,
        "_comment": "that's all"
    },
    "numb_steps": 1000000,
    "seed": 10,
    "disp_file": "lcurve.out",
    "disp_freq": 1000,
    "save_freq": 10000,
    "_comment": "that's all"
}

}

[wanghan-iapcm]

have you used exactly the same vasp settings for the initial and augmentation datasets?

what is the training and validation accuracy for the initial and augmentation datasets?

[ikuki-ikuki]

Thanks for your reply，
I dont understand the meaning of "accuracy for the initial and augmentation".All my AIMD datasets are training under the same settings by vasp5.4.4. Here are my specific script parameter settings.
ISTART = 0
LWAVE = .FALSE.
LCHARG = .FALSE.
ENCUT = 500
NELM = 200
EDIFF = 1E-4
NSW = 800
NBLOCK = 1
KBLOCK = 1
ISYM = 0
ISIF = 2
POTIM = 3
TEBEG = 350
SMASS = 0
MDALGO = 2
ISMEAR = 1
SIGMA = 0.2
IALGO = 48
ALGO = N
LREAL = T
IBRION = 0
NCORE = 8
LSCALAPACK = .FALSE.
All parameters except simulation temperature(TEBEG) and step size(POTIM) are consistent. I've verified that all results demonstrate final convergence.
Additionally, I have actually conducted more AIMD calculations, including:

• ​​17,528 entries​​ for bulk/slab/perturbed substrate materials
• ​​17,106 entries​​ for Li-substrate interfaces
• ​​18,895 entries​​ for pristine Li systems

All data subsets successfully described their respective systems ​​when trained separately for specialized potentials​​. However, when integrated into a unified potential function, ​​the combined dataset fails to accurately describe any system​​ – even previously trained models become unusable for MD simulations.
Could this be related to the training steps or learning rate settings in my training process?