PiNNwall enables the integration of a machine learned charge response kernel (CRK) predicted by PiNN1 with the Metalwalls MD simulation software2. PiNNwall was first introduced in the following work3, and later upgraded through the introduction of higher-order tensorial features in PiNN4.
PiNNwall predicts the "Hessian" Matrix for Metalwalls2 using machine learning (ML)-models implemented in PiNN1. In order to run PiNNwall, one needs to have PiNN installed first.
Before prediction, one needs to have constructed the input files of Metalwalls, namely the data.inpt and runtime.inpt, which you will use to run the Metalwalls simulations. For the best performance of PiNNwall, base charges need for the electrode atoms need to be set in the data.inpt file. Depending on the electrode structure, these could directly be taken from the PiNNwall papers, predicted using PiNN, taken from force-field parameters or computed using a population analysis method with DFT.
Then, clone this repo to get the scripts and the ML-models that will be used in predicting the CRK.
After executing PiNNwall, it will generate a hessian_matrix.inpt to be used by Metalwalls which contains the machine learned CRK for the given electrode model. PiNNwall will also update the hardness, and the cutoff parameters in the runtime.inpt to ensure that consistency between PiNN and Metalwalls necessary when using Metalwalls to run the MD simulations.
Note, to achieve performance consistent with the PiNNwall paper, a modified version of Metalwalls must be used. This will be made available upon request.
python pinnwall [-p <MODEL_DIR>] [-i <WORKING_DIR>] [-m [<methodename>]] [-o <filename>]Options:
-p <MODEL_DIR> (./trained_models)
path to the trained pinn model
-i <WORKING_DIR> (./)
path to the input files of MW
-m [<methodename>] (eem)
List of model used to compute the CRK to construct the Hessian Matrix employed by Metalwalls
-o <filename> (pinnwall.out)
log of pinnwall, default to inputs_dir
Executing produces:
- hessian_matrix.inpt - charge response kernel file to be used by Metalwalls
- hessian_matrix.out - human-readable charge response kernel file
- pinnwall.out - text file containing the parameters used for this run
To start a Metalwalls simulation, ensure that you have placed the hessian_maxtrix.inpt file generated by PiNNwall in the run directory, along with the modified runtime.inpt file. Also place the data.inpt file you used when executing PiNNwall in the same run directory. Then, execute Metalwalls as usual.
The current ML-models provided with PiNNwall were trained using the QM7b dataset. This means that presently, PiNNwall is only able to predict sensible Hessian matrices for electrodes containing C, H, N, O, S, and Cl. You are encouraged to train models for different systems using the polarizability models included in the PiNN package. For this, the PiNet2 network is recommended. More information on how to do this can be found in the PiNN documentation5. These models can then easily be integrated into PiNNwall by placing them into the trained_models folder.
We recommend that you use the EEM models for carbonaceous system as this seems to yield the most physical performance when comparing a carbon electrode to a perfect metal system. More information on the performance of all model types can be found in the PiNN papers 34.
A more in-depth tutorial illustrating the application of PiNNwall to a hydroxylated electrode can be found here and more usages beyond what is shown on this landing page can be found here .
Footnotes
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Dufils, T., Knijff, L., Shao, Y., & Zhang, C. (2023). PiNNwall: Heterogeneous electrode models from integrating machine learning and atomistic simulation. Journal of Chemical Theory and Computation, 19(15), 5199-5209. ↩ ↩2
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Li, J., Knijff, L., Zhang, Z. Y., Andersson, L., & Zhang, C. (2025). PiNN: Equivariant Neural Network Suite for Modeling Electrochemical Systems. Journal of Chemical Theory and Computation, 21(3), 1382-1395. ↩ ↩2