{ "cells": [ { "cell_type": "markdown", "id": "e583a102-6ae2-4390-a8a1-96b9170844eb", "metadata": { "tags": [] }, "source": [ "## Create the systems\n", "\n", "### Create the ring molecule from SMILES\n", "\n", "First, we create ``ring_mol`` from the SMILES code, and then we cut the O-C and N-C bonds and increase the distance between the fragments. We need to first cut the bonds before increasing the distance, since ``ChemicalSystem.set_distance`` requires separate molecule fragments to work on." ] }, { "cell_type": "code", "execution_count": 1, "id": "d10cd3db-aa41-4d7e-b318-74fc801bc51f", "metadata": { "tags": [] }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from scm.plams import view\n", "from scm.base import ChemicalSystem\n", "\n", "# from_smiles creates bonds between atoms\n", "ring_mol = ChemicalSystem.from_smiles(\"C1CON=N1\")\n", "view(\n", " ring_mol,\n", " width=350,\n", " height=350,\n", " direction=\"tilt_pca3\",\n", " show_atom_labels=True,\n", " atom_label_type=\"Name\",\n", ")" ] }, { "cell_type": "markdown", "id": "d93f148b-2770-4d01-93f2-49a61e8c024d", "metadata": {}, "source": [ "### Identify which bonds to cut\n", "\n", "It's easiest if you know the indices of the bonded atoms. Then you can just cut those bonds, e.g. ``ring_mol.bonds.remove_bonds_between_atoms(0, 4)``. That would remove the N-C bond in the above picture (there the indices start with 1, but when calling Python methods they start from 0)\n", "\n", "That is fine and the most straightforward way to cut the bonds. However, it is difficult to automate because the atomic indices may change for different structures.\n", "\n", "Here we show a way to programmatically identify \"an N atom that is bonded to another N and C\", and \"an O atom that is bonded to an N and C\". Then we cut those corresponding bonds." ] }, { "cell_type": "code", "execution_count": 2, "id": "94b371df-ab2e-44bd-bc16-0b996bc1bde9", "metadata": {}, "outputs": [], "source": [ "from typing import List, Tuple, Iterable, Optional\n", "\n", "\n", "def find_first_matching(\n", " cs: ChemicalSystem, elem: str, neighbors: Iterable[str]\n", ") -> Optional[Tuple[int, List[int], List[str]]]:\n", " \"\"\"Finds the first atom with element ``elem`` and neighbors ``neighbors``.\n", "\n", " Returns a 3-tuple: (index_of_atom, list_of_neighbor_indices, list_of_neighbor_elements)\n", "\n", " In the return value the list_of_neighbor_indices and list_of_neighbor_elements\n", " have the same length and correspond to each other.\n", " \"\"\"\n", "\n", " sorted_target_neighbors = sorted(neighbors)\n", " for i, at in enumerate(cs):\n", " if at.symbol != elem:\n", " continue\n", " my_neighbors = list(cs.bonds.get_bonded_atoms(i))\n", " my_neighbors_elems = [cs.atoms[j].symbol for j in my_neighbors]\n", " if sorted(my_neighbors_elems) == sorted_target_neighbors:\n", " return i, my_neighbors, my_neighbors_elems\n", "\n", " return None" ] }, { "cell_type": "code", "execution_count": 3, "id": "6ea2ffc2-2aa9-4365-b774-ec2dea666081", "metadata": {}, "outputs": [], "source": [ "sep_mol = ring_mol.copy()\n", "\n", "# N bonded to N and C\n", "iN, N_neighs, N_neighs_elems = find_first_matching(sep_mol, \"N\", [\"N\", \"C\"])\n", "iN_C = N_neighs[N_neighs_elems.index(\"C\")] # index of neighboring C atom\n", "\n", "# O bonded to N and C\n", "iO, O_neighs, O_neighs_elems = find_first_matching(sep_mol, \"O\", [\"N\", \"C\"])\n", "iO_C = O_neighs[O_neighs_elems.index(\"C\")] # index of neighboring C atom" ] }, { "cell_type": "code", "execution_count": 4, "id": "e7478a08-19de-4158-85da-750654e2fa88", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "I will cut the bonds between atoms 4-0 and 2-1 (zero-based indexing)\n" ] } ], "source": [ "print(f\"I will cut the bonds between atoms {iN}-{iN_C} and {iO}-{iO_C} (zero-based indexing)\")" ] }, { "cell_type": "markdown", "id": "804f383e-a2f8-4781-8317-58ec5ad82d4f", "metadata": {}, "source": [ "### Cut the bonds" ] }, { "cell_type": "code", "execution_count": 5, "id": "643bbfb0-c6d7-44aa-a649-d67624dd2e72", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sep_mol.bonds.remove_bonds_between_atoms(iN, iN_C)\n", "sep_mol.bonds.remove_bonds_between_atoms(iO, iO_C)\n", "view(sep_mol, width=150, height=150, direction=\"tilt_pca3\")" ] }, { "cell_type": "markdown", "id": "62c105be-7327-4bfd-913e-c5d87b4bd1b2", "metadata": {}, "source": [ "### Increase the distance \n", "\n", "Now increase the distance. When you change the distance, all distances within the separate molecules stay the same." ] }, { "cell_type": "code", "execution_count": 6, "id": "6ecf83ed-bc7c-4ffd-b502-2cd2cf146ad2", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sep_mol.set_distance(iN, iN_C, 2.5) # angstrom\n", "view(sep_mol, width=150, height=150, direction=\"tilt_pca3\")" ] }, { "cell_type": "markdown", "id": "159b0a83-66ca-418e-88be-c56bb873d837", "metadata": {}, "source": [ "### Preoptimize the separated and ring systems" ] }, { "cell_type": "code", "execution_count": 7, "id": "5466550c-d6e7-4b49-8252-ca518553f50f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[28.02|09:33:25] JOB preopt_ring_mol STARTED\n", "[28.02|09:33:25] JOB preopt_ring_mol RUNNING\n", "[28.02|09:33:26] JOB preopt_ring_mol FINISHED\n", "[28.02|09:33:26] JOB preopt_ring_mol SUCCESSFUL\n", "[28.02|09:33:26] JOB preopt_sep_mol STARTED\n", "[28.02|09:33:26] JOB preopt_sep_mol RUNNING\n", "[28.02|09:33:28] JOB preopt_sep_mol FINISHED\n", "[28.02|09:33:28] JOB preopt_sep_mol SUCCESSFUL\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from scm.plams import Settings, AMSJob, view\n", "\n", "preopt_s = Settings()\n", "preopt_s.input.ams.Task = \"GeometryOptimization\"\n", "preopt_s.input.DFTB.Model = \"GFN1-xTB\"\n", "preopt_ring_job = AMSJob(settings=preopt_s, molecule=ring_mol, name=\"preopt_ring_mol\")\n", "preopt_ring_job.run()\n", "preopt_sep_job = AMSJob(settings=preopt_s, molecule=sep_mol, name=\"preopt_sep_mol\")\n", "preopt_sep_job.run();" ] }, { "cell_type": "markdown", "id": "b7a59bb1-a96f-4ea0-ad17-1b09a559bf05", "metadata": {}, "source": [ "## Final molecule results" ] }, { "cell_type": "code", "execution_count": 8, "id": "1b2a2214-5696-4a58-9118-a6afeae7b99d", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "opt_ring_mol = preopt_ring_job.results.get_main_system()\n", "view(opt_ring_mol, width=150, height=150, direction=\"tilt_pca3\")" ] }, { "cell_type": "code", "execution_count": 9, "id": "b6b76655-3e6e-4d1e-a3dd-e619ba9c2bef", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "opt_sep_mol = preopt_sep_job.results.get_main_system()\n", "view(opt_sep_mol, width=150, height=150, direction=\"tilt_pca3\")" ] }, { "cell_type": "markdown", "id": "c1441413-2d28-406c-8e35-8eb51975870e", "metadata": {}, "source": [ "## Copy-pasteable system blocks\n", "\n", "When running calculations, bonds are really only used in ForceField calculations. Otherwise, you do not need any bonding information (it is not used). So it can be a bit misleading to store it or print it. Therefore, we here remove all the bonds before printing." ] }, { "cell_type": "code", "execution_count": 10, "id": "f6462d74-0828-4081-b0cb-0d2064edc6d6", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "System\n", " Atoms\n", " C 0.8176518265162523 0.006185364744877703 0.11652129166388352\n", " C -0.5531099061901967 -0.6363379504647356 -0.08529176454211394\n", " O -1.4164729952124286 0.48607450912407413 -0.19677333330677277\n", " N -0.6859030532374636 1.632537947036214 -0.08098563281192021\n", " N 0.5074709667559635 1.4283821326486141 0.08689807666792455\n", " H 1.2798721321383 -0.22791534308315498 1.0777663111036928\n", " H 1.5305100901334066 -0.2099050616627963 -0.6819652771198405\n", " H -0.866292408825807 -1.2485848049850283 0.7685478972017538\n", " H -0.6137266520780276 -1.2304367933580558 -1.0047175688566132\n", " End\n", "End\n" ] } ], "source": [ "opt_ring_mol.bonds.clear_bonds()\n", "print(opt_ring_mol)" ] }, { "cell_type": "code", "execution_count": 11, "id": "9f2e7847-a2d6-499a-a0d4-631b5b8f420d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "System\n", " Atoms\n", " C 1.0065881512963437 -1.417955004240563 0.12885420257373528\n", " C -0.08873018725926754 -2.1350000996605933 -0.03448649693154524\n", " O -1.5081108856471217 1.002080375323799 -0.2045570136880532\n", " N -0.4803377947492119 1.514182931120784 -0.052917508314934095\n", " N 0.4992559331764258 2.001851603909277 0.09160944741412266\n", " H 1.3565548327663348 -1.1143852514314225 1.1071131753303691\n", " H 1.6148659535066119 -1.095826278535031 -0.7065218137603257\n", " H -0.6985763255773102 -2.454234856430313 0.8007371044735704\n", " H -0.4402537210929026 -2.435675244924285 -1.0129788309893883\n", " End\n", "End\n" ] } ], "source": [ "opt_sep_mol.bonds.clear_bonds()\n", "print(opt_sep_mol)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.12" } }, "nbformat": 4, "nbformat_minor": 5 }