Reactions Discovery¶

#!/usr/bin/env amspython
# coding: utf-8

# ## Initial imports

from typing import List
import scm.plams as plams
from scm.input_classes import engines
from scm.reactions_discovery import ReactionsDiscoveryJob
from rdkit import Chem
from rdkit.Chem import Draw

# Settings for displaying molecules in the notebook
from rdkit.Chem.Draw import IPythonConsole

IPythonConsole.ipython_useSVG = True
IPythonConsole.molSize = 250, 250


# ## Helpers for showing molecules


def draw_molecules(molecules: List[plams.Molecule]):
    smiles = [molecule.properties.smiles for molecule in molecules]
    return draw_smiles(smiles)


def draw_smiles(smiles: List[str]):
    rd_mols = [Chem.MolFromSmiles(s) for s in smiles]
    return Draw.MolsToGridImage(rd_mols)


# ## The ReactionsDiscoveryJob class

job = ReactionsDiscoveryJob(name="MyDiscovery")
driver = job.input
md = driver.MolecularDynamics


# ## Setting up the reactants for molecular dynamics

md.NumSimulations = 4
build = md.BuildSystem
build.NumAtoms = 250
build.Density = 0.9
build.Molecule[0].SMILES = "O"  # Water
build.Molecule[0].MoleFraction = 1
build.Molecule[1].SMILES = "NCCO"  # MEA
build.Molecule[1].MoleFraction = 2
build.Molecule[2].SMILES = "O=C=O"  # Carbondioxide
build.Molecule[2].MoleFraction = 3
draw_smiles([build.Molecule[i].SMILES.val for i in range(len(build.Molecule))])


# ## Setting up reactive molecular dynamics

md.Enabled = "Yes"
md.Type = "NanoReactor"
reactor = md.NanoReactor
reactor.NumCycles = 10
reactor.Temperature = 500
reactor.MinVolumeFraction = 0.6


# ## Setting up network extraction and ranking

network = driver.NetworkExtraction
network.Enabled = "Yes"
network.UseCharges = "Yes"
ranking = driver.ProductRanking
ranking.Enabled = "Yes"


# ## Selecting the AMS engine to use

engine = engines.ReaxFF()
engine.ForceField = "Glycine.ff"
engine.TaperBO = "Yes"  # This is a really important setting for reaction analysis with ReaxFF potentials
driver.Engine = engine


# ## Running reactions discovery

result = job.run()  # start the job
job.check()  # check if job was succesful


# ## Obtain the results

graph, molecules, categories = result.get_network()


# ## Categories
#
# The categories are `Products` `Reactants` and `Unstable`, as described in the reactions discovery manual. `molecules` is a dictionairy with keys equal to the categories and each concomitant value is a list of PLAMS molecules.

print(categories)


draw_molecules(molecules["Reactants"])


# ## Products
#
# These are the side products that reactions discovery found in the order as found by the ranking algorithm.

draw_molecules(molecules["Products"][:6])


# ## Unstable
#
# Unstable products were determined to not likely exist outside of reactive dynamics. This e.g. includes radicals or structures that don't form stable molecules in isolation. Not all unstable molecules have a sensible 2d structure, so instead we plot their 3d structure.

for unstable_molecule in molecules["Unstable"][:3]:
    plams.plot_molecule(unstable_molecule)


# ## Graph of the reaction network
#
# The graph is a bipartate networkx DiGraph with reaction and molecule nodes. This can be stored on disk in standard graph formats, e.g. `.gml`

import networkx as nx

nx.write_gml(graph, "reaction_network.gml")


# ## Load a job not originally run by PLAMS

job = ReactionsDiscoveryJob.load_external("plams_workdir/MyDiscovery")


graph, molecules, categories = job.results.get_network()