conformer search with solvation effect

arc/main.py

@@ -81,6 +81,7 @@
                                          instead (e.g., ``opt_level``).
         composite_method (str, dict, Level, optional): Composite method.
         conformer_level (str, dict, Level, optional): Level of theory for conformer searches.
+        conf_generation_level (str, dict, Level, optional): Level of theory for conformer generation.
         opt_level (str, dict, Level, optional): Level of theory for geometry optimization.
         freq_level (str, dict, Level, optional): Level of theory for frequency calculations.
         sp_level (str, dict, Level, optional): Level of theory for single point calculations.
@@ -167,6 +168,7 @@
         level_of_theory (str): A shortcut representing either sp//geometry levels or a composite method.
         composite_method (Level): Composite method.
         conformer_level (Level): Level of theory for conformer searches.
+        conf_generation_level (Level): Level of theory for conformer generation.
         opt_level (Level): Level of theory for geometry optimization.
         freq_level (Level): Level of theory for frequency calculations.
         sp_level (Level): Level of theory for single point calculations.
@@ -242,6 +244,7 @@
                  compute_thermo: bool = True,
                  compute_transport: bool = False,
                  conformer_level: Optional[Union[str, dict, Level]] = None,
+                 conf_generation_level: Optional[Union[str, dict, Level]] = None,
                  dont_gen_confs: List[str] = None,
                  e_confs: float = 5.0,
                  ess_settings: Dict[str, Union[str, List[str]]] = None,
@@ -342,6 +345,7 @@
         self.level_of_theory = level_of_theory
         self.composite_method = composite_method or None
         self.conformer_level = conformer_level or None
+        self.conf_generation_level = conf_generation_level or None
         self.opt_level = opt_level or None
         self.freq_level = freq_level or None
         self.sp_level = sp_level or None
@@ -485,6 +489,8 @@
             restart_dict['compute_transport'] = self.compute_transport
         if self.conformer_level is not None:
             restart_dict['conformer_level'] = self.conformer_level.as_dict()
+        if self.conf_generation_level is not None:
+            restart_dict['conf_generation_level'] = self.conf_generation_level.as_dict()
         if self.dont_gen_confs:
             restart_dict['dont_gen_confs'] = self.dont_gen_confs
         if self.ts_adapters:
@@ -569,15 +575,21 @@
             Status dictionary indicating which species converged successfully.
         """
         logger.info('\n')
+        considered_list = list()
         for species in self.species:
             if not isinstance(species, ARCSpecies):
                 raise ValueError(f'All species must be ARCSpecies objects. Got {type(species)}')
             if species.is_ts:
                 logger.info(f'Considering transition state: {species.label}')
             else:
-                logger.info(f'Considering species: {species.label}')
-                if species.mol is not None:
-                    display(species.mol.copy(deep=True))
+                if not species.multi_species:
+                    logger.info(f'Considering species: {species.label}')
+                    if species.mol is not None:
+                        display(species.mol.copy(deep=True))
+                elif species.multi_species not in considered_list:
+                    logger.info(f'Considering species: {species.multi_species}')
+                    considered_list.append(species.multi_species)
+
         logger.info('\n')
         for rxn in self.reactions:
             if not isinstance(rxn, ARCReaction):
@@ -587,6 +599,7 @@
                                    rxn_list=self.reactions,
                                    composite_method=self.composite_method,
                                    conformer_level=self.conformer_level,
+                                   conf_generation_level=self.conf_generation_level,
                                    opt_level=self.opt_level,
                                    freq_level=self.freq_level,
                                    sp_level=self.sp_level,
@@ -963,6 +976,11 @@
         self.conformer_level = Level(repr=self.conformer_level)
         logger.info(f'Conformers:{default_flag} {self.conformer_level}')
 
+        if self.conf_generation_level is not None:
+            default_flag = ''
+            self.conf_generation_level = Level(repr=self.conf_generation_level)
+            logger.info(f'Conformers_generation:{default_flag} {self.conf_generation_level}')
+
         if self.reactions or any([spc.is_ts for spc in self.species]):
             if not self.ts_guess_level:
                 self.ts_guess_level = default_levels_of_theory['ts_guesses']

arc/scheduler.py

@@ -130,6 +130,7 @@ class Scheduler(object):
         project_directory (str): Folder path for the project: the input file path or ARC/Projects/project-name.
         composite_method (str, optional): A composite method to use.
         conformer_level (Union[str, dict], optional): The level of theory to use for conformer comparisons.
+        conf_generation_level (Union[str, dict], optional): The level of theory to use for conformer generation.
         opt_level (Union[str, dict], optional): The level of theory to use for geometry optimizations.
         freq_level (Union[str, dict], optional): The level of theory to use for frequency calculations.
         sp_level (Union[str, dict], optional): The level of theory to use for single point energy calculations.
@@ -201,6 +202,7 @@ class Scheduler(object):
                         Allowed values are He, Ne, Ar, Kr, H2, N2, O2.
         composite_method (str): A composite method to use.
         conformer_level (dict): The level of theory to use for conformer comparisons.
+        conf_generation_level (dict): The level of theory to use for conformer generation.
         opt_level (dict): The level of theory to use for geometry optimizations.
         freq_level (dict): The level of theory to use for frequency calculations.
         sp_level (dict): The level of theory to use for single point energy calculations.
@@ -229,6 +231,7 @@ def __init__(self,
                  project_directory: str,
                  composite_method: Optional[Level] = None,
                  conformer_level: Optional[Level] = None,
+                 conf_generation_level: Optional[Level] = None,
                  opt_level: Optional[Level] = None,
                  freq_level: Optional[Level] = None,
                  sp_level: Optional[Level] = None,
@@ -308,6 +311,7 @@ def __init__(self,
         self.servers = list()
         self.composite_method = composite_method
         self.conformer_level = conformer_level
+        self.conf_generation_level = conf_generation_level
         self.ts_guess_level = ts_guess_level
         self.opt_level = opt_level
         self.freq_level = freq_level
@@ -424,8 +428,9 @@ def __init__(self,
                 if not self.job_types['conformers'] and len(species.conformers) == 1:
                     # conformers weren't asked for, assign initial_xyz
                     species.initial_xyz = species.conformers[0]
-                if species.label not in self.running_jobs:
-                    self.running_jobs[species.label if not species.multi_species else species.multi_species] = list()
+                label = species.label if not species.multi_species else species.multi_species
+                if label not in self.running_jobs.keys():
+                    self.running_jobs[label] = list()
                 if self.output[species.label]['convergence']:
                     continue
                 if species.is_monoatomic():
@@ -563,9 +568,9 @@ def schedule_jobs(self):
                                     self.determine_most_stable_conformer(label)  # also checks isomorphism
                                 if self.species_dict[label].initial_xyz is not None:
                                     # if initial_xyz is None, then we're probably troubleshooting conformers, don't opt
-                                    if not self.composite_method:
+                                    if not self.composite_method and (self.job_types['opt'] or self.job_types['fine']):
                                         self.run_opt_job(label, fine=self.fine_only)
-                                    else:
+                                    elif self.composite_method and self.job_types['composite']:
                                         self.run_composite_job(label)
                             self.timer = False
                             break
@@ -1098,7 +1103,10 @@ def run_conformer_jobs(self, labels: Optional[List[str]] = None):
                         n_confs=n_confs,
                         e_confs=self.e_confs,
                         plot_path=os.path.join(self.project_directory, 'output', 'Species',
-                                               label, 'geometry', 'conformers'))
+                                               label, 'geometry', 'conformers'),
+                        conf_generation_level=self.conf_generation_level if self.conf_generation_level is not None else None,
+                        conf_path=os.path.join(self.project_directory, 'calcs', 'Species', f'{label}_multi'),
+                        )
                 self.process_conformers(label)
             # TSs:
             elif self.species_dict[label].is_ts \

arc/settings/settings.py

@@ -175,6 +175,9 @@
                     }
 
 default_levels_of_theory = {'conformer': 'wb97xd/def2svp',  # it's recommended to choose a method with dispersion
+                            'conf_generation': {'method': 'UFF',
+                                                'solvation_method': 'SMD',
+                                                'solvent': 'water'}, # good default for Gaussian
                             'ts_guesses': 'wb97xd/def2svp',
                             'opt': 'wb97xd/def2tzvp',  # good default for Gaussian
                             # 'opt': 'wb97m-v/def2tzvp',  # good default for QChem

arc/species/conformers.py

@@ -38,6 +38,7 @@
 
 import copy
 import logging
+import os
 import sys
 import time
 from itertools import product
@@ -53,16 +54,21 @@
 from rmgpy.molecule.molecule import Atom, Bond, Molecule
 from rmgpy.molecule.element import C as C_ELEMENT, H as H_ELEMENT, F as F_ELEMENT, Cl as Cl_ELEMENT, I as I_ELEMENT
 
-from arc.common import (convert_list_index_0_to_1,
+from arc.common import (ARC_PATH,
+                        convert_list_index_0_to_1,
                         determine_top_group_indices,
                         get_single_bond_length,
                         generate_resonance_structures,
                         logger,
+                        save_yaml_file,
+                        read_yaml_file,
                         )
 from arc.exceptions import ConformerError, InputError
+from arc.job.local import execute_command
 import arc.plotter
+from arc.level import Level
 from arc.species import converter, vectors
-
+from arc.parser import parse_xyz_from_file
 
 # The number of conformers to generate per range of heavy atoms in the molecule
 # (will be increased if there are chiral centers)
@@ -164,6 +170,8 @@
                         return_all_conformers=False,
                         plot_path=None,
                         print_logs=True,
+                        conf_generation_level=None,
+                        conf_path=None,
                         ) -> Optional[Union[list, Tuple[list, list]]]:
     """
     Generate conformers for (non-TS) species starting from a list of RMG Molecules.
@@ -195,6 +203,8 @@
                                    If None, the plot will not be shown (nor saved).
         print_logs (bool, optional): Whether define a logger so logs are also printed to stdout.
                                      Useful when run outside of ARC. True to print.
+        conf_generation_level (str, optional): The level of conformer generation to perform.
+        conf_path (str, optional): A folder path in which the conformers will be saved.
 
     Raises:
         ConformerError: If something goes wrong.
@@ -267,7 +277,7 @@
         torsions, tops = determine_rotors(mol_list)
     conformers = generate_force_field_conformers(
         mol_list=mol_list, label=label, xyzs=xyzs, torsion_num=len(torsions), charge=charge, multiplicity=multiplicity,
-        num_confs=num_confs_to_generate, force_field=force_field)
+        num_confs=num_confs_to_generate, force_field=force_field, conf_generation_level=conf_generation_level, conf_path=conf_path)
 
     lowest_confs = list()
     if len(conformers):
@@ -630,6 +640,8 @@
                                     xyzs=None,
                                     num_confs=None,
                                     force_field='MMFF94s',
+                                    conf_generation_level=None,
+                                    conf_path=None,
                                     ) -> List[dict]:
     """
     Generate conformers using RDKit and OpenBabel and optimize them using a force field
@@ -644,6 +656,8 @@
         multiplicity (int): The species spin multiplicity.
         num_confs (int, optional): The number of conformers to generate.
         force_field (str, optional): The type of force field to use.
+        conf_generation_level (str, optional): The level of conformer generation to perform.
+        conf_path (str, optional): A folder path in which the conformers will be saved.
 
     Raises:
         ConformerError: If xyzs is given and it is not a list, or its entries are not strings.
@@ -673,6 +687,12 @@
         except ValueError as e:
             logger.warning(f'Could not generate conformers for {label}, failed with: {e}')
         if ff_xyzs:
+            if conf_generation_level is not None and conf_generation_level.solvent is not None:
+                ff_xyzs, ff_energies = get_force_field_energies_solvation(label,
+                                                                          ff_xyzs,
+                                                                          conf_generation_level=conf_generation_level,
+                                                                          conf_path=conf_path,
+                                                                          )
             for xyz, energy in zip(ff_xyzs, ff_energies):
                 conformers.append({'xyz': xyz,
                                    'index': len(conformers),
@@ -1153,6 +1173,75 @@
     return xyzs, energies
 
 
+def get_force_field_energies_solvation(label: str,
+                                       ff_xyzs: list,
+                                       conf_generation_level: Level,
+                                       conf_path: str,
+                                       ) -> Tuple[list, list]:
+    """
+    Determine force field energies with solvation effect using Gaussian.
+
+    Args:
+        label (str): The species' label.
+        ff_xyzs (list): Entries are xyz coordinates in dict format.
+        conf_generation_level (dict): The level of solvation to use.
+        conf_path (str): A folder path in which the conformers will be saved.
+
+    Returns:
+        Tuple[list, list]:
+            - Entries are xyz coordinates, each in a dict format.
+            - Entries are the FF energies (in kJ/mol).
+    """
+    ARC_child_path = conf_path
+    content = dict()
+    content['project'] = f'conf_gen_multi_{label}'
+    content['opt_level'] = {'method': conf_generation_level.method,
+                            'solvation_method': conf_generation_level.solvation_method or 'SMD',
+                            'solvent': conf_generation_level.solvent,
+                            }
+    content['report_e_elect'] = True
+    content['job_types'] = {'opt': True,
+                            'sp': False,
+                            'fine': False,
+                            'freq': False,
+                            'rotors': False,
+                            'conformers': False,
+                            }
+    content['n_confs'] = 1
+    content['compute_thermo'] = False
+    content['species'] = list()
+    species_per_job = 500
+    for i, xyz in enumerate(ff_xyzs):
+        spc_dict = dict()
+        spc_dict['label'] = f'{label}_multi_{i}'
+        spc_dict['xyz'] = xyz
+        spc_dict['multi_species'] = f'{label}_multi_cluster_{i//species_per_job}'
+        content['species'].append(spc_dict)
+    save_yaml_file(path=os.path.join(ARC_child_path, 'input.yml'), content=content)
+    commands = ['source ~/.bashrc', 'conda activate arc_env', f'python {ARC_PATH}/ARC.py {ARC_child_path}/input.yml']
+    command = '; '.join(commands)
+    stdout, stderr = execute_command(command=command, shell=True, executable='/bin/bash')
+    with open(os.path.join(ARC_child_path, 'stdout.txt'), 'w') as f:
+        stdout_str = '\n'.join(str(x) for x in stdout)
+        f.write(stdout_str)
+    with open(os.path.join(ARC_child_path, 'stderr.txt'), 'w') as f:
+        stderr_str = '\n'.join(str(x) for x in stderr)
+        f.write(stderr_str)
+    if len(stderr) > 0 or len(stdout) == 0:
+        logger.warning(f'Got the following error when trying to submit job:\n{stderr}.')
+    xyzs = list()
+    energies = list()
+    content = dict()
+    energy_dict = read_yaml_file(os.path.join(ARC_child_path, 'output', 'e_elect_summary.yml'))
+    for i in range(len(ff_xyzs)):
+        xyzs.append(parse_xyz_from_file(os.path.join(ARC_child_path, 'output', 'Species', f'{label}_multi_{i}', 'geometry', f'{label}_multi_{i}.xyz')))
+        energies.append(energy_dict[f'{label}_multi_{i}'])
+        content[f'{label}_multi_{i}'] = {'xyz': xyzs[-1], 'energy': energies[-1]}
+    save_yaml_file(path=os.path.join(ARC_child_path, 'output', 'energy_geometry_summary.yml'), content=content)
+    logger.info(f'{label} conformer with solvation effect are spawned from a subprocess.')
+    return xyzs, energies
+
+
 def openbabel_force_field_on_rdkit_conformers(label, rd_mol, force_field='MMFF94s', optimize=True):
     """
     Optimize RDKit conformers by OpenBabel using a force field (MMFF94 or MMFF94s are recommended).