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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       "<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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [id, status, chemicalformula, job, subjob, projectpath, project, timestart, timestop, totalcputime, computer, hamilton, hamversion, parentid, masterid]\n",
       "Index: []"
      ]
     },
     "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: 738914\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": {
      "text/html": [
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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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>738914</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>workshop_preparation/potentials/01-EAM/AlFit/</td>\n",
       "      <td>2022-05-30 12:02:13.150799</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  738914  finished            None  TrainData  /TrainData   \n",
       "\n",
       "                           projectpath  \\\n",
       "0  /nfshome/leimeroth/pyiron/projects/   \n",
       "\n",
       "                                         project                  timestart  \\\n",
       "0  workshop_preparation/potentials/01-EAM/AlFit/ 2022-05-30 12:02:13.150799   \n",
       "\n",
       "  timestop totalcputime           computer           hamilton hamversion  \\\n",
       "0     None         None  pyiron@mogli243#1  TrainingContainer        0.4   \n",
       "\n",
       "  parentid masterid  \n",
       "0     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": {
      "text/html": [
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       "\n",
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       "        vertical-align: top;\n",
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       "\n",
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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": {
      "image/png": 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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": 7,
   "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": 8,
   "id": "ada4baf2-cede-4f31-915d-500b6bcb3518",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7ff52fa41760>]"
      ]
     },
     "execution_count": 8,
     "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": 11,
   "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": 12,
   "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": 13,
   "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": 13,
     "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": 14,
   "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": 15,
   "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: 738917\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": [
       "554.137132378037"
      ]
     },
     "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": 25,
   "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"
   ]
  }
 ],
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