Adsorption Clustering and Diffusion on substrate

[nsysu7433111]

Dear DeepMD-kit Developers and Team,

Our laboratory extensively uses DeepMD-kit for research at the nanoscale. We truly appreciate the team's efforts in developing high-precision potentials, which have significantly advanced our work in the field of atomic materials.

However, despite extensive attempts, I have been unable to achieve stable adsorption on 2D materials using our current DeepMD model. I am currently encountering the following specific issues:

1. LAMMPS Minimization:

Clustering: Lithium ions tend to cluster during the minimization process (see Figure 1).
Blue Atom is Boron、Yellow Atom is Carbon、Red Atom is Arsenic、Purple Atom is Lithium
Before minimize

After minimize(Figure 1)

Incorrect Sites: Lithium atoms migrate to metastable (non-optimal) adsorption sites rather than the most stable positions.

2. LAMMPS NPT Simulation:

Substrate Instability: Atoms from the substrate are pulled away/detached by the adsorbed Lithium above (see Figure 2).
Before NPT

NPT at step 4400(Figure 2) (some substrate atom are pulled away the yellow and blue atom is substrate atom)

Surface Diffusion: Lithium ions begin to diffuse across the surface unstably.

Intercalation: Lithium atoms intercalate into the interlayer spacing of the substrate.
Before NPT

NPT at step 2200 (Lithium atoms intercalate into the interlayer)

3. Model Deviation: Observing max_devi_f in the md.out file, the model shows high deviation when describing Lithium clustering and diffusion:

If the simulation does not crash, the max_devi_f for Li clustering and diffusion ranges between 0.4 and 3.

For Lithium intercalation events, the deviation spikes as high as 40 eV/Å.

Thank you for your time and assistance.

Best regards,
Shih Tsao CHANG

[dosubot[bot]]

Hi @nsysu7433111! I'm Dosu and I’m helping the deepmd-kit team.

Your issues—Li clustering, incorrect adsorption, substrate instability, and high model deviation—are classic signs that the DeepMD model is extrapolating beyond its training data or lacks coverage of key atomic environments. High max_devi_f values (especially spikes up to 40 eV/Å) directly indicate the model is uncertain or unreliable for these configurations, which can cause unphysical results like clustering, unstable diffusion, or substrate atom detachment [source].

This typically happens when the training set does not sufficiently sample all relevant configurations: Li adsorption sites, surface diffusion pathways, intercalation events, and substrate-adsorbate interactions. Even small gaps or low diversity in the training data can lead to large errors, especially in multi-element systems or when new environments (like Li at a surface or in an interlayer) are encountered [source].

To address this, you should:

• Expand your training data to include diverse configurations covering all relevant adsorption, diffusion, and intercalation scenarios. Use active learning (e.g., DP-GEN) to iteratively sample and add high-deviation structures from your MD runs [source].
• Check that your force and energy labels are processed correctly and that your descriptor settings (cutoff, smoothing, 'sel' parameter) and loss weights are appropriate for your system [source].
• Validate your model with dp test RMSE and monitor model deviation during simulations. High deviation means you need more representative data for those configurations [source].
• For simulation setup, ensure your LAMMPS scripts are correct (e.g., freeze substrate layers if needed to diagnose instability), and filter out unphysical structures in early exploration [source].

For model deviation: DeepMD-kit computes max_devi_f by comparing force predictions from multiple models; high values mean the model is extrapolating. You can output per-atom and relative deviations in LAMMPS to pinpoint problematic regions [source]. See the LAMMPS interface docs for details on keywords like atomic and relative.

If you can share more about your training data coverage, model settings, and whether you use active learning, I can help with more targeted suggestions.

To reply, just mention @dosu.

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[nsysu7433111]

@dosu
My training data contain
Substrate: 1. BC2As and BC2P at 50,300K vc-md 2. BC2As and BC2P scale 3. Dimer
Adsorption system: 1. BC2As and BC2P Adsorption Single Li in different super cell(1x1 2x2 3x3 4x4) at 50,300K
DP-GEN: 1. BC2As and BC2P Adsorption 4 Li on Top or Bot surface 2. BC2As and BC2P Adsorption 4Li on Top and Bot surface
model settings
{
"learning_rate" : {
"start_lr" : 2.9961807945733e-08,
"stop_lr" : 3e-08,
"decay_steps" : 7387,
"_comment" : "that's all"
},
"_comment" : "that's all",
"model" : {
"type_map" : [
"As",
"B",
"C",
"Li",
"N",
"P"
],
"descriptor" : {
"type" : null,
"rcut" : null,
"rcut_smth" : null,
"list" : [
{
"seed" : 1830526208,
"rcut_smth" : 2,
"type" : "se_e2_a",
"sel" : "auto",
"rcut" : 6,
"resnet_dt" : false,
"axis_neuron" : 16,
"neuron" : [
20,
40,
80
],
"set_davg_zero" : true
},
{
"type" : "se_e3",
"rcut_smth" : 2,
"seed" : 1,
"set_davg_zero" : true,
"neuron" : [
4,
8,
16
],
"resnet_dt" : false,
"sel" : "auto",
"rcut" : 4
}
],
"seed" : 15797
},
"_comment" : " that's all",
"fitting_net" : {
"atom_ener" : [
0,
0,
0,
0,
0,
0
],
"_comment" : " that's all",
"resnet_dt" : true,
"seed" : 20467,
"neuron" : [
240,
240,
240
]
}
},
"loss" : {
"_comment" : " that's all",
"start_pref_e" : 0.001,
"start_pref_v" : 100,
"limit_pref_f" : 1,
"limit_pref_e" : 1,
"limit_pref_v" : 0.1,
"start_pref_f" : 1000
},
"set_prefix" : "set",
"batch_size" : "auto"
},
"save_ckpt" : "model_compress.ckpt",
"disp_training" : true
}
}

active learning? you mean DP-GEN right? yes i use the DP-GEN

[dosubot[bot]]

Your issues—Li clustering, unstable adsorption, substrate detachment, and high model deviation—are shared by others working on similar systems. The main cause is usually insufficient or unrepresentative training data for key atomic environments, especially for challenging cases like Li intercalation, clustering, or substrate instability. Even with DP-GEN, if your exploration doesn't sample these failed or high-deviation events, the model will extrapolate and produce unphysical results, as seen in your high max_devi_f values and simulation crashes (example).

To improve reliability: systematically check if your training data covers not just stable adsorption, but also Li clusters, intercalated Li, substrate atoms near Li, and any failed or high-deviation structures from your MD runs. Use per-atom model deviation output in LAMMPS to pinpoint which atoms/environments are most uncertain (how-to). Expand your DP-GEN exploration to intentionally include these problematic cases, not just stable ones.

Consider increasing your descriptor neuron sizes (e.g., to [25, 50, 100]) for better model capacity (maintainer advice). Also, review your loss weights—especially for force and virial—to ensure balanced training; sometimes tuning these helps with stability (example). If you haven't already, try freezing substrate atoms in LAMMPS to isolate whether instability is due to the model or simulation setup.

Could you clarify: does your DP-GEN workflow include failed or high-deviation cases (e.g., substrate detachment, Li intercalation), or only stable adsorption? Have you tried freezing substrate atoms to diagnose the source of instability? This will help target the next steps.

To reply, just mention @dosu.

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[nsysu7433111]

@dosu
No,DP-GEN didn't contain high-deviation cases(e.g., substrate detachment, Li intercalation)
DP-GEN contain two Li atom cluster and some stable adsorption case or lithium at different position but is not adsorb on the surface
i didn't try freezing substrate atoms to diagnose the source of instability

[dosubot[bot]]

Your model issues are likely because your DP-GEN workflow doesn't include high-deviation or failed structures (like substrate detachment or Li intercalation), so the model can't learn those environments and will extrapolate unphysically. To fix this, use DP-GEN's adaptive trust level parameters to systematically select and add high-deviation cases to your training set. Set model_devi_f_trust_lo close to your training force error and model_devi_f_trust_hi to 3–5× higher, or use model_devi_adapt_trust_lo with model_devi_numb_candi_f to automatically pick frames with the largest force deviations for labeling and retraining. This will help your model learn from problematic configurations and improve reliability for adsorption, diffusion, and intercalation scenarios [details].

You can also use the simplify workflow in DP-GEN to identify and add data with large model deviations to your dataset for retraining [details]. Monitor model deviation during exploration, and whenever you see high values (especially for Li clustering, substrate detachment, or intercalation), label those frames with DFT and add them to your training set.

To diagnose if instability is due to the model or simulation setup, try freezing the substrate atoms in your LAMMPS input (e.g., using fix setforce 0.0 0.0 0.0 on substrate groups). If instability disappears, the issue is likely with the model's description of substrate-adsorbate interactions; if not, check your simulation parameters.

Iteratively refine your workflow: track which configurations are added, monitor model deviation, and keep expanding your training set with challenging cases until the model deviation drops and simulations become stable.

To reply, just mention @dosu.

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