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README
gp_Ag_00
gp_Ag_30
gp_Au_00
gp_Au_01
gp_Au_30
gp_CuAg_00
gp_CuAg_30
gp_Cu_00
gp_Cu_30
gp_PdAg_00
gp_PdAg_30
gp_Pd_00
gp_Pd_30
nn_AgAu_01
nn_AgAu_31
nn_Ag_01
nn_Ag_30
nn_Ag_31
nn_Au_01
nn_Au_31
nn_CuAgAu_01
nn_CuAgAu_31
nn_CuAg_01
nn_CuAg_30
nn_CuAg_31
nn_CuAu_01
nn_CuAu_31
nn_CuPdAg_01
nn_CuPdAg_30
nn_CuPdAg_31
nn_CuPd_01
nn_CuPd_30
nn_CuPd_31
nn_Cu_01
nn_Cu_30
nn_Cu_31
nn_MgCa_30
nn_PdAg_01
nn_PdAg_30
nn_PdAg_31
nn_Pd_01
nn_Pd_30
nn_Pd_31

README

		|=======================================================================|
		|									|
       	       	|            Interatomic potentials library for MAISE code              |
		|	     		    	   	      	       	     		|
		|		   Alexey Kolmogorov's research group			|
		|	   	          Binghamton University				|
		|									|
		|                  https://github.com/maise-guide/maise			|
		|=======================================================================|


a) nn_* models are neural network based potentials, trained with the MAISE code. They are named as:

   nn_(system name: series of elements)_(dimensionality of the dataset used for the training)(model version)

   e.g.:   nn_CuPdAg_30 is a NN model for the Cu-Pd-Ag ternary constructed using the 3D (bulk) dataset.
           nn_CuAu_00   is a NN model for the Cu-Au system trained on the bulk and nanoparticle datasets.

   note:   1- Currently, models named as *0 are trained using 30-component input vector and with 6 Ang cut-off; 
      while *1 models are trained using 51-component input vector and 7.5 Ang cut- off.



b) gp_* models are Gupta potential parameters, collected from the literature, and named as:

   gp_(system name: series of elements)_(dimensionality of the system that model is prepared for)(model version)

   note:   1- GP models are collected from the literature (cited in the reference entry in the model file)
              and were originally for nanoparticles.
           2- The cut-off range of the models are adjusted to be usable for the bulk systems, hence _3# models are created.
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