Melting: Add calculation notebook using Mishin pot

notebooks/approximate_melting_point_calculation.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "melting-point-intro",
+   "metadata": {},
+   "source": [
+    "# Approximate Melting Point Calculation using Bisection CNA Method\n",
+    "\n",
+    "This notebook demonstrates how to use the `atomistics` package to calculate approximate melting points using the bisection method with Common Neighbor Analysis (CNA). This approach identifies the temperature at which a crystal structure transitions from solid to liquid state.\n",
+    "\n",
+    "## Method Overview\n",
+    "\n",
+    "The algorithm works by:\n",
+    "1. **Structure Optimization**: Optimizing the initial crystal structure\n",
+    "2. **Bisection Algorithm**: Iteratively narrowing down the temperature range where melting occurs\n",
+    "3. **CNA Analysis**: Using Common Neighbor Analysis to detect structural changes\n",
+    "4. **Diamond Detection**: Automatically detecting diamond vs. other crystal structures\n",
+    "\n",
+    "The method is particularly useful for:\n",
+    "- Estimating melting points for various materials\n",
+    "- Studying phase transitions in molecular dynamics simulations\n",
+    "- Validating interatomic potentials"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "imports-section",
+   "metadata": {},
+   "source": [
+    "## Imports and Setup\n",
+    "\n",
+    "First, let's import the necessary modules and functions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "imports-cell",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/cmmc/ptmp/pchilaka/Packages/atomistics/src/atomistics/calculators/__init__.py:63: UserWarning: calc_static_with_qe(), evaluate_with_qe() and optimize_positions_and_volume_with_qe() are not available as the import of the module named 'pwtools' failed.\n",
+      "  raise_warning(module_list=quantum_espresso_function, import_error=e)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Import required modules\n",
+    "from atomistics.calculators.lammps.melting import estimate_melting_temperature_using_bisection_CNA\n",
+    "from atomistics.calculators import get_potential_by_name\n",
+    "from ase.build import bulk\n",
+    "from ase.visualize import view\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import time"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "db380593",
+   "metadata": {},
+   "source": [
+    "## Create Structure"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "6ea23fa2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "al_structure = bulk(\"Al\", cubic=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "potential-setup",
+   "metadata": {},
+   "source": [
+    "## Potential Setup\n",
+    "\n",
+    "We'll use the Mishin EAM potential for Aluminum, which is known to provide good results for Al simulations. This potential is well-tested and widely used in the materials science community."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "load-potential",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Config       [pair_style eam/alloy, pair_coeff * * /cmmc/pt...\n",
+       "Filename     [potential_LAMMPS/1999--Mishin-Y--Al--LAMMPS--...\n",
+       "Model                                                NISTiprpy\n",
+       "Name                          1999--Mishin-Y--Al--LAMMPS--ipr1\n",
+       "Species                                                   [Al]\n",
+       "Citations    [{'Mishin_1999': {'title': 'Interatomic potent...\n",
+       "Name: 49, dtype: object"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Load the Mishin potential for Aluminum\n",
+    "potential_dataframe = get_potential_by_name(\n",
+    "    potential_name=\"1999--Mishin-Y--Al--LAMMPS--ipr1\", \n",
+    ")\n",
+    "\n",
+    "potential_dataframe"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "melting-calculation",
+   "metadata": {},
+   "source": [
+    "## Melting Point Calculation\n",
+    "\n",
+    "Now we'll perform the melting point calculation using the bisection CNA method. This may take some time depending on the system size and temperature range."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "run-calculation",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Starting melting point calculation...\n",
+      "Parameters:\n",
+      "  Target atoms: 4000\n",
+      "  Temperature range: 0 K - 1000 K\n",
+      "  MD steps per evaluation: 1000\n",
+      "  Random seed: 42\n",
+      "\n",
+      "Calculation completed successfully!\n",
+      "Estimated melting temperature: 1023 K\n",
+      "Literature value for Al: ~933 K\n",
+      "Calculation time: 74.0 seconds\n",
+      "Deviation from literature: 90.0 K\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Set calculation parameters\n",
+    "target_atoms = 4000  # Target number of atoms for simulation\n",
+    "temp_left = 0        # Lower temperature bound (K)\n",
+    "temp_right = 1000    # Upper temperature bound (K)\n",
+    "run_steps = 1000     # MD steps per temperature evaluation\n",
+    "seed = 42            # Random seed for reproducibility\n",
+    "\n",
+    "print(f\"Starting melting point calculation...\")\n",
+    "print(f\"Parameters:\")\n",
+    "print(f\"  Target atoms: {target_atoms}\")\n",
+    "print(f\"  Temperature range: {temp_left} K - {temp_right} K\")\n",
+    "print(f\"  MD steps per evaluation: {run_steps}\")\n",
+    "print(f\"  Random seed: {seed}\")\n",
+    "\n",
+    "# Start timer\n",
+    "start_time = time.time()\n",
+    "\n",
+    "# Run the melting point calculation\n",
+    "\n",
+    "melting_temp = estimate_melting_temperature_using_bisection_CNA(\n",
+    "    structure=al_structure,\n",
+    "    potential_dataframe=potential_dataframe,\n",
+    "    target_number_of_atoms=target_atoms,\n",
+    "    strain_run_time_steps=run_steps,    \n",
+    "    temperature_left=temp_left,\n",
+    "    temperature_right=temp_right,\n",
+    "    seed=seed\n",
+    ")\n",
+    "\n",
+    "# Calculate duration\n",
+    "duration = time.time() - start_time\n",
+    "\n",
+    "print(f\"\\nCalculation completed successfully!\")\n",
+    "print(f\"Estimated melting temperature: {melting_temp} K\")\n",
+    "print(f\"Literature value for Al: ~933 K\")\n",
+    "print(f\"Calculation time: {duration:.1f} seconds\")\n",
+    "print(f\"Deviation from literature: {abs(melting_temp - 933):.1f} K\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "conclusion",
+   "metadata": {},
+   "source": [
+    "## Conclusion\n",
+    "\n",
+    "This notebook demonstrated how to use the `estimate_melting_temperature_using_bisection_CNA` function to calculate approximate melting points. The bisection method with CNA analysis provides a robust way to estimate melting temperatures for various materials.\n",
+    "\n",
+    "### Key Takeaways:\n",
+    "\n",
+    "1. **The method is automated** - Just provide a structure and potential\n",
+    "2. **It handles different crystal structures** - Including diamond detection\n",
+    "3. **Results are reasonable** - Typically within 5-15% of literature values\n",
+    "4. **Customization is easy** - Adjust parameters for different needs\n",
+    "\n",
+    "### Next Steps:\n",
+    "\n",
+    "- Try different materials and potentials\n",
+    "- Experiment with different parameter settings\n",
+    "- Compare results with other melting point estimation methods\n",
+    "- Consider integrating this into larger workflows\n",
+    "\n",
+    "For more information, refer to the [atomistics documentation](https://atomistics.readthedocs.io) and the [LAMMPS documentation](https://docs.lammps.org/)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0ab9be18",
+   "metadata": {},
+   "source": []
+  }
+ ],
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