HandsOnPotenitalFitting.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5f91f58f-ef53-484d-9951-045829872567",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyiron import Project, ase_to_pyiron\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0dde0404-8501-4649-ae81-3f008d4a76e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "pr = Project(\"AlFit\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97f1ec7a-4eb6-404d-afdb-0d2739255ead",
   "metadata": {},
   "source": [
    "### Training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "af1bbd9f-c61b-4bc8-b122-5848537620fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>status</th>\n",
       "      <th>chemicalformula</th>\n",
       "      <th>job</th>\n",
       "      <th>subjob</th>\n",
       "      <th>projectpath</th>\n",
       "      <th>project</th>\n",
       "      <th>timestart</th>\n",
       "      <th>timestop</th>\n",
       "      <th>totalcputime</th>\n",
       "      <th>computer</th>\n",
       "      <th>hamilton</th>\n",
       "      <th>hamversion</th>\n",
       "      <th>parentid</th>\n",
       "      <th>masterid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>738591</td>\n",
       "      <td>finished</td>\n",
       "      <td>None</td>\n",
       "      <td>TrainData</td>\n",
       "      <td>/TrainData</td>\n",
       "      <td>/nfshome/leimeroth/pyiron/projects/</td>\n",
       "      <td>PotentialsProject/EAMAlLi/AlFit/</td>\n",
       "      <td>2022-05-28 08:46:42.723038</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pyiron@mogli243#1</td>\n",
       "      <td>TrainingContainer</td>\n",
       "      <td>0.4</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id    status chemicalformula        job      subjob  \\\n",
       "0  738591  finished            None  TrainData  /TrainData   \n",
       "\n",
       "                           projectpath                           project  \\\n",
       "0  /nfshome/leimeroth/pyiron/projects/  PotentialsProject/EAMAlLi/AlFit/   \n",
       "\n",
       "                   timestart timestop totalcputime           computer  \\\n",
       "0 2022-05-28 08:46:42.723038     None         None  pyiron@mogli243#1   \n",
       "\n",
       "            hamilton hamversion parentid masterid  \n",
       "0  TrainingContainer        0.4     None     None  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pr.job_table()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b434f0c0-4110-41f5-8f05-33140376f2f6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The job TrainData was saved and received the ID: 738591\n"
     ]
    }
   ],
   "source": [
    "tc = pr.create.job.TrainingContainer(\"TrainData\", delete_existing_job=True)\n",
    "df = pd.read_pickle(\"Al_3PreFinal.pckl.gz\")\n",
    "for r in df.itertuples():\n",
    "    s = ase_to_pyiron(r.ase_atoms)\n",
    "    tc.add_structure(structure=s, energy=r.energy_corrected, forces=r.forces, name=f\"ID{r.Index}\")\n",
    "tc.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9678bb53-b9e6-4d37-9ada-5331af8ecf69",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>status</th>\n",
       "      <th>chemicalformula</th>\n",
       "      <th>job</th>\n",
       "      <th>subjob</th>\n",
       "      <th>projectpath</th>\n",
       "      <th>project</th>\n",
       "      <th>timestart</th>\n",
       "      <th>timestop</th>\n",
       "      <th>totalcputime</th>\n",
       "      <th>computer</th>\n",
       "      <th>hamilton</th>\n",
       "      <th>hamversion</th>\n",
       "      <th>parentid</th>\n",
       "      <th>masterid</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>738591</td>\n",
       "      <td>finished</td>\n",
       "      <td>None</td>\n",
       "      <td>TrainData</td>\n",
       "      <td>/TrainData</td>\n",
       "      <td>/nfshome/leimeroth/pyiron/projects/</td>\n",
       "      <td>PotentialsProject/EAMAlLi/AlFit/</td>\n",
       "      <td>2022-05-28 08:48:58.579656</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pyiron@mogli243#1</td>\n",
       "      <td>TrainingContainer</td>\n",
       "      <td>0.4</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id    status chemicalformula        job      subjob  \\\n",
       "0  738591  finished            None  TrainData  /TrainData   \n",
       "\n",
       "                           projectpath                           project  \\\n",
       "0  /nfshome/leimeroth/pyiron/projects/  PotentialsProject/EAMAlLi/AlFit/   \n",
       "\n",
       "                   timestart timestop totalcputime           computer  \\\n",
       "0 2022-05-28 08:48:58.579656     None         None  pyiron@mogli243#1   \n",
       "\n",
       "            hamilton hamversion parentid masterid  \n",
       "0  TrainingContainer        0.4     None     None  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pr.job_table()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "48a7d045-7935-4970-bea9-19e5ad77e316",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>V</th>\n",
       "      <th>E</th>\n",
       "      <th>space_group</th>\n",
       "      <th>crystal_system</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.484415</td>\n",
       "      <td>-3.482751</td>\n",
       "      <td>225</td>\n",
       "      <td>cubic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14.835973</td>\n",
       "      <td>-3.433909</td>\n",
       "      <td>225</td>\n",
       "      <td>cubic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15.165661</td>\n",
       "      <td>-3.452735</td>\n",
       "      <td>225</td>\n",
       "      <td>cubic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15.495350</td>\n",
       "      <td>-3.466526</td>\n",
       "      <td>225</td>\n",
       "      <td>cubic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15.825038</td>\n",
       "      <td>-3.475815</td>\n",
       "      <td>225</td>\n",
       "      <td>cubic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>316</th>\n",
       "      <td>28.793489</td>\n",
       "      <td>-2.746083</td>\n",
       "      <td>1</td>\n",
       "      <td>triclinic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>317</th>\n",
       "      <td>29.358067</td>\n",
       "      <td>-2.708848</td>\n",
       "      <td>1</td>\n",
       "      <td>triclinic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>318</th>\n",
       "      <td>29.922645</td>\n",
       "      <td>-2.672118</td>\n",
       "      <td>1</td>\n",
       "      <td>triclinic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>30.487223</td>\n",
       "      <td>-2.635916</td>\n",
       "      <td>1</td>\n",
       "      <td>triclinic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320</th>\n",
       "      <td>31.051801</td>\n",
       "      <td>-2.600260</td>\n",
       "      <td>1</td>\n",
       "      <td>triclinic</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>321 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             V         E  space_group crystal_system\n",
       "0    16.484415 -3.482751          225          cubic\n",
       "1    14.835973 -3.433909          225          cubic\n",
       "2    15.165661 -3.452735          225          cubic\n",
       "3    15.495350 -3.466526          225          cubic\n",
       "4    15.825038 -3.475815          225          cubic\n",
       "..         ...       ...          ...            ...\n",
       "316  28.793489 -2.746083            1      triclinic\n",
       "317  29.358067 -2.708848            1      triclinic\n",
       "318  29.922645 -2.672118            1      triclinic\n",
       "319  30.487223 -2.635916            1      triclinic\n",
       "320  31.051801 -2.600260            1      triclinic\n",
       "\n",
       "[321 rows x 4 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tc.plot.energy_volume(crystal_systems=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "512673b5-e606-40d8-8a1d-1a9932591f3b",
   "metadata": {},
   "source": [
    "### Add training data\n",
    "Instead of simply loading the training data as is weights are assigned to the structures based on their energy this time. Additionally the structure with a very high atomic volume is filtered out.  \n",
    "Structures with low energy get a higher weight than structures with high energy.\n",
    "It can also help to increase the weights of special properties or important structures to put a focus on things that are important for the scope of the potential.\n",
    "For a list of a all properties that can be fitted refer to the atomicrex website https://www.atomicrex.org/properties.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "9a11b349-5094-4cbc-a03e-e929753e4a82",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_e_weight(E_S, Delta=1.0, N=2):\n",
    "    return 1 / (E_S + Delta) ** N"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ada4baf2-cede-4f31-915d-500b6bcb3518",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f92943da520>]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "E_S = np.arange(0, 50, 0.1)\n",
    "plt.plot(E_S, get_e_weight(E_S))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "73da8023-4aa1-4329-8c81-fb6b294c7cb1",
   "metadata": {},
   "outputs": [],
   "source": [
    "tc = pr.load(\"TrainData\")\n",
    "job = pr.create.job.Atomicrex(\"SampleJob\", delete_existing_job=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7126c3a1-87a7-4978-9241-de69bb609437",
   "metadata": {},
   "outputs": [],
   "source": [
    "for s, energy, forces, name in tc.iter(\"energy\", \"forces\", \"identifier\"):\n",
    "    natoms = len(s)\n",
    "    volume = s.get_volume(per_atom=True)\n",
    "    if volume > 400:\n",
    "        continue\n",
    "    e = energy / natoms\n",
    "    weight = get_e_weight(e+3.5) # add approx. equilibrium energy of Al\n",
    "    job.structures.add_structure(structure=s, identifier=name, relative_weight=weight)\n",
    "    job.structures.add_scalar_fit_property(prop=\"atomic-energy\", target_val=e,)\n",
    "    job.structures.add_vector_fit_property(prop=\"atomic-forces\", target_val=forces)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f655bcb-9926-4d92-ad86-7b84c312661a",
   "metadata": {},
   "source": [
    "### Set the potential type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3940e14a-77a0-4448-97cc-f5b5b8428c0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "job.potential = job.factories.potentials.eam_potential()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45ad4c4f-a496-467d-93e1-dda611625af3",
   "metadata": {},
   "source": [
    "### Define functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "23f79603-b52b-44a7-a93d-d5dd0dbf97c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# morse A is the original rather simple morse potential\n",
    "morseA = job.factories.functions.morse_A(\"MorseA\", D0=0.15, r0=3.0, alpha=2.0, species=[\"Al\", \"Al\"])\n",
    "morseA.screening = job.factories.functions.x_pow_n_cutoff(\"morseScreen\", cutoff=7.6, species=[\"Al\", \"Al\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "ad4399f1-26b2-4d99-b7b3-b12821cfb26d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 720x504 with 1 Axes>,\n",
       " <AxesSubplot:xlabel='r [$\\\\AA$]', ylabel='func(r)'>)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "morseA.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ea68594-64f3-4728-94d5-dba8677cb9d7",
   "metadata": {},
   "source": [
    "Electron density as used by Mishin in his Cu potential (https://doi.org/10.1103/PhysRevB.63.224106, Gaussian + exp(-$\\beta$r term)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "2171010d-9c36-42be-b9e5-27b0508aaa15",
   "metadata": {},
   "outputs": [],
   "source": [
    "rho = job.factories.functions.MishinCuRho(identifier=\"Rho\", a=1.0, r1=-1, r2=0.0, beta1=1.0, beta2=3.0, species=[\"Al\", \"Al\"])\n",
    "rho.screening = job.factories.functions.x_pow_n_cutoff(\"RhoScreen\", cutoff=7.6, species=[\"Al\", \"Al\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "dd3dc2e1-94be-4b74-9adc-3666f417bc50",
   "metadata": {},
   "outputs": [],
   "source": [
    "F = job.factories.functions.user_function(identifier=\"F\", input_variable=\"rho\", species=[\"Al\"])\n",
    "# User function for embedding term\n",
    "F = job.factories.functions.user_function(identifier=\"F\", input_variable=\"r\")\n",
    "F.expression = \"-A*sqrt(r)\"\n",
    "F.derivative = \"-A/(2*sqrt(r))\"\n",
    "F.parameters.add_parameter(\"A\", start_val=2.3, min_val=0.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "32e048b2-562d-4b9a-ba2a-2712450a70f0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The job SampleJob was saved and received the ID: 738592\n"
     ]
    }
   ],
   "source": [
    "job.potential.pair_interactions[morseA.identifier] = morseA\n",
    "job.potential.electron_densities[rho.identifier] = rho\n",
    "job.potential.embedding_energies[F.identifier] = F\n",
    "\n",
    "job.input.fit_algorithm = job.factories.algorithms.ar_lbfgs(max_iter=1000, gradient_epsilon=1e-9)\n",
    "\n",
    "job.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abf288b4-a8e8-4695-9e37-c911711e501e",
   "metadata": {},
   "source": [
    "### Examples for alternative minimizers\n",
    "\n",
    "Local:\n",
    "```\n",
    "job.input.fit_algorithm = job.factories.algorithms.ln_neldermead(max_iter=10000)\n",
    "```\n",
    "nelder mead is a bit more robust than the LBFGS implementation in atomicrex\n",
    "It is gradient free, so each iteration is much quicker, but more iterations are needed\n",
    "\n",
    "Global:\n",
    "```\n",
    "job.input.fit_algorithm = job.factories.algorithms.gn_esch(max_iter=10000)\n",
    "```\n",
    "ESCH is an evolutionary algorithm. To use it all parameters need a min and a max value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "156e4c38-5636-4719-852b-bce9cf45e24d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 576x1296 with 3 Axes>,\n",
       " array([[<AxesSubplot:title={'center':'Al F'}, xlabel='$\\\\rho $ [a.u.]'>],\n",
       "        [<AxesSubplot:title={'center':'Al rho_AlAl'}, xlabel='r [$\\\\AA$]'>],\n",
       "        [<AxesSubplot:title={'center':'Al V_AlAl'}, xlabel='r [$\\\\AA$]'>]],\n",
       "       dtype=object))"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x1296 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "job.plot_final_potential()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b74ef18b-e4c1-4e91-a82a-16340c8869ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "555.5878759578943"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(job.output.iterations, job.output.residual)\n",
    "job.output.residual[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2ca61e19-d5a3-47b2-af15-339d9ffa6675",
   "metadata": {},
   "outputs": [],
   "source": [
    "plots = job.plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c4b28c40-9e63-4805-aeb8-805abc044478",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,7))\n",
    "plots.energy_scatter_histogram()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "774a4082-7a72-4062-8371-3953f9c826b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plots.force_scatter_histogram()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "caee172a-24a9-43cd-a122-eb09120d2684",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plots.force_scatter_histogram(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "67951e44-0b40-493e-ab36-b96053966b5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plots.force_scatter_histogram(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "0c32e69e-9f2b-48b4-b8e8-8d1e39db0062",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plots.force_scatter_histogram(axis=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21cf9787-4b08-49d6-a0c8-638dff820788",
   "metadata": {},
   "source": [
    "# Appendix\n",
    "\n",
    "For the potential used in the validation and phase diagram calculations a different functional form was used.\n",
    "\n",
    "The Al potential followed the same formalism as used by Mishin in his Cu potential https://doi.org/10.1103/PhysRevB.63.224106.\n",
    "For Li and AlLi terms the pair interaction was described using a morse-C potential, while cubic splines were used for the electron densities.\n",
    "The Li embedding term was a modified version of that used by Mishin that is implemented in the atomicrex code.\n",
    "\n",
    "To be able to guarantee certain properties the parameters of these functions were constrained.\n",
    "EAM potentials are invariant under the transformations:  \n",
    "$\\rho(r)\\rightarrow s\\rho(r)$, $F(\\bar\\rho)\\rightarrow F(\\bar\\rho / s)$ and $F(\\bar\\rho)\\rightarrow F(\\bar\\rho) - g\\bar\\rho$, $V(r)\\rightarrow V(r)+2g\\rho(r)$\n",
    "\n",
    "To uniquely define them the condition:\n",
    "$\\sum_m N_m \\rho(r_m) = 1$ was applied. Here $N_m$ is the number of neighbors of the equilibrium structure in shell m.\n",
    "\n",
    "\n",
    "A perfect lattice constant can be obtained by applying the mechanical equilibrium condition:  \n",
    "$\\frac{1}{2} \\sum_m N_m V(r_m)' r_m + F_0' \\sum_m N_m \\rho(r_m)' r_m$ where $F0$ is the value of the embedding function at the equilibrium structures electron density, i.e. $F(1)$ if the above condition is applied.\n",
    "\n",
    "The correct equilibrium energy $E_0$ can be guaranteed with:  \n",
    "$F_0 = E_0-\\frac{1}{2}\\sum_m N_m V(r_m)$\n",
    "\n",
    "An exact fit to the bulk modulus can be achieved by applying:  \n",
    "$\\frac{1}{2}\\sum_m N_mV(r_m)''r_m^2 + F_0' \\sum_m N_m \\rho(r_m)'' R_m^2 + F_0''(\\sum_m N_m \\rho(r_m)' r_m)^2 $\n",
    "\n",
    "These (or similar equations) have to be solved with respect to some parameter in the function. A simple example is the electron density which can be constrained to 1 by multiplying the whole function with some factor c after its evaluation for the equilibrium structure.\n",
    "This then looks f.e. like this in the atomicrex input file (xml format):\n",
    "\n",
    "```\n",
    "        <constraint id=\"one_constrain_rhoSpline_LiLi\" dependent-dof=\"EAM.rho_LiLi.cfactor_rhoSpline_LiLi.const\">\n",
    "            <expression>1 / ( + 8 * {EAM.rho_LiLi.rhoSpline_LiLi:eval.2.966650281143688} + 6 * {EAM.rho_LiLi.rhoSpline_LiLi:eval.3.425592676819574} + 12 * {EAM.rho_LiLi.rhoSpline_LiLi:eval.4.844519622724197} + 24 * {EAM.rho_LiLi.rhoSpline_LiLi:eval.5.6807027967998565} + 8 * {EAM.rho_LiLi.rhoSpline_LiLi:eval.5.933300562287376} + 6 * {EAM.rho_LiLi.rhoSpline_LiLi:eval.6.851185353639148} + 24 * {EAM.rho_LiLi.rhoSpline_LiLi:eval.7.465906149994755})</expression>\n",
    "        </constraint>\n",
    "```\n",
    "The block contains an identifier, the \"path\" to the parameter that is constrained and the expression that is applied to constrain it.\n",
    "In the pyiron interface such constraints can be added using as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "0f10fa0c-e266-4064-99c7-94db3d739d7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "job.input.parameter_constraints.add_constraint(\n",
    "    identifier=\"Identifier\",\n",
    "    dependent_dof=\"The parameter or degree of freedom to constrain\",\n",
    "    expression=\"Expression that is used to determine the value of the parameter, parsed using muparser\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13c05737-5e54-4c3d-af08-149d174e1790",
   "metadata": {},
   "source": [
    "If any questions regarding the interface/feature requests/problems occur after the workshop feel free to send me a mail leimeroth@mm.tu-darmstadt.de"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3f95f557-3c4e-4a08-b3d5-5d4b784b08b0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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