Define conformer single point calculation after optimization

arc/checks/common.py

@@ -36,8 +36,10 @@
         Optional[int]: The corresponding conformer or tsg index.
     """
     i = None
-    if 'conformer' in job_name:
+    if 'conf_opt' in job_name:
         i = int(job_name[9:])
+    elif 'conf_sp' in job_name:
+        i = int(job_name[8:])
     elif 'tsg' in job_name:
         i = int(job_name[3:])
     return i

arc/checks/common_test.py

@@ -36,9 +36,9 @@ def test_get_i_from_job_name(self):
         """Test the get_i_from_job_name() function"""
         self.assertIsNone(common.get_i_from_job_name(''))
         self.assertIsNone(common.get_i_from_job_name('some_job_name'))
-        self.assertEqual(common.get_i_from_job_name('conformer3'), 3)
-        self.assertEqual(common.get_i_from_job_name('conformer33'), 33)
-        self.assertEqual(common.get_i_from_job_name('conformer3355'), 3355)
+        self.assertEqual(common.get_i_from_job_name('conf_opt_3'), 3)
+        self.assertEqual(common.get_i_from_job_name('conf_opt_33'), 33)
+        self.assertEqual(common.get_i_from_job_name('conf_opt_3355'), 3355)
         self.assertEqual(common.get_i_from_job_name('tsg2'), 2)
 
 

arc/common.py

@@ -92,8 +92,8 @@ def initialize_job_types(job_types: Optional[dict] = None,
         if 'fine_grid' in job_types:
             del job_types['fine_grid']
 
-    defaults_to_true = ['conformers', 'fine', 'freq', 'irc', 'opt', 'rotors', 'sp']
-    defaults_to_false = ['bde', 'onedmin', 'orbitals']
+    defaults_to_true = ['conf_opt', 'fine', 'freq', 'irc', 'opt', 'rotors', 'sp']
+    defaults_to_false = ['conf_sp', 'bde', 'onedmin', 'orbitals']
     if job_types is None:
         job_types = default_job_types
         logger.info("Job types were not specified, using ARC's defaults")

arc/common_test.py

@@ -43,13 +43,14 @@ def setUpClass(cls):
         cls.maxDiff = None
         cls.rmgdb = make_rmg_database_object()
         load_families_only(cls.rmgdb)
-        cls.default_job_types = {'conformers': True,
+        cls.default_job_types = {'conf_opt': True,
                                  'opt': True,
                                  'fine': True,
                                  'freq': True,
                                  'sp': True,
                                  'rotors': True,
                                  'irc': True,
+                                 'conf_sp': False,
                                  'orbitals': False,
                                  'onedmin': False,
                                  'bde': False,
@@ -388,7 +389,7 @@ def test_almost_equal_lists(self):
 
     def test_initialize_job_with_given_job_type(self):
         """Test the initialize_job_types() function"""
-        job_types = {'conformers': False, 'opt': True, 'fine': True, 'freq': True, 'sp': False, 'rotors': False, 'irc': True}
+        job_types = {'conf_opt': False, 'opt': True, 'fine': True, 'freq': True, 'sp': False, 'rotors': False, 'irc': True}
         job_types_expected = copy.deepcopy(self.default_job_types)
         job_types_expected.update(job_types)
         job_types_initialized = common.initialize_job_types(job_types)

arc/job/adapter.py

@@ -110,7 +110,8 @@ class JobTypeEnum(str, Enum):
     The available jon types are a finite set.
     """
     composite = 'composite'
-    conformers = 'conformers'  # conformer optimization (not generation)
+    conf_opt = 'conf_opt'  # conformer optimization (not generation)
+    conf_sp = 'conf_sp'  # conformer single point
     freq = 'freq'
     gen_confs = 'gen_confs'  # conformer generation
     irc = 'irc'
@@ -385,9 +386,9 @@ def determine_job_array_parameters(self):
             if self.species is not None:
                 if len(self.species) > 1:
                     self.iterate_by.append('species')
-                if job_type == 'conformers':
+                if job_type == 'conf_opt':
                     if self.species is not None and sum(len(species.conformers) for species in self.species) > 10:
-                        self.iterate_by.append('conformers')
+                        self.iterate_by.append('conf_opt')
                         self.number_of_processes += sum([len(species.conformers) for species in self.species])
                 for species in self.species:
                     if job_type in ['sp', 'opt', 'freq', 'optfreq', 'composite', 'ornitals', 'onedmin', 'irc']:
@@ -456,7 +457,7 @@ def write_hdf5(self):
             else:
                 for species in self.species:
                     data[species.label] = list()
-                    if 'conformers' in self.iterate_by:
+                    if 'conf_opt' in self.iterate_by:
                         for conformer in species.conformers:
                             data[species.label].append(DataPoint(charge=species.charge,
                                                                  job_types=['opt'],
@@ -693,10 +694,8 @@ def _set_job_number(self):
             self.job_num = job_num
         # 2. Set other related attributes job_name and job_server_name.
         self.job_server_name = self.job_server_name or 'a' + str(self.job_num)
-        if self.conformer is not None and (self.job_name is None or 'conformer_a' in self.job_name):
-            if self.job_name is not None:
-                logger.warning(f'Replacing job name {self.job_name} with conformer{self.conformer}')
-            self.job_name = f'conformer{self.conformer}'
+        if self.conformer is not None and self.job_name is None:
+            self.job_name = f'{self.job_type}_{self.conformer}_{self.job_server_name}'
         elif self.tsg is not None and (self.job_name is None or 'tsg_a' in self.job_name):
             if self.job_name is not None:
                 logger.warning(f'Replacing job name {self.job_name} with tsg{self.conformer}')
@@ -1097,7 +1096,7 @@ def _log_job_execution(self):
                 local = 'local '
             else:
                 server = f' on {self.server}'
-        if 'conformer' in self.job_name or 'tsg' in self.job_name:
+        if 'conf_opt' in self.job_name or 'tsg' in self.job_name:
             job_server_name = f' ({self.job_server_name})'
         execution_type = {'incore': 'incore job', 'queue': 'queue job', 'pipe': 'job array (pipe)'}[self.execution_type]
         pivots = f' for pivots {[[tor[1] + 1, tor[2] + 1] for tor in self.torsions]}' if self.torsions is not None else ''

arc/job/adapter_test.py

@@ -53,7 +53,8 @@ def test_job_enum(self):
     def test_job_type_enum(self):
         """Test the JobTypeEnum class"""
         self.assertEqual(JobTypeEnum('composite').value, 'composite')
-        self.assertEqual(JobTypeEnum('conformers').value, 'conformers')
+        self.assertEqual(JobTypeEnum('conf_opt').value, 'conf_opt')
+        self.assertEqual(JobTypeEnum('conf_sp').value, 'conf_sp')
         self.assertEqual(JobTypeEnum('freq').value, 'freq')
         self.assertEqual(JobTypeEnum('gen_confs').value, 'gen_confs')
         self.assertEqual(JobTypeEnum('irc').value, 'irc')
@@ -123,7 +124,7 @@ def setUpClass(cls):
         """
         cls.maxDiff = None
         cls.job_1 = GaussianAdapter(execution_type='queue',
-                                    job_type='conformers',
+                                    job_type='conf_opt',
                                     level=Level(method='cbs-qb3'),
                                     project='test',
                                     project_directory=os.path.join(ARC_PATH, 'arc', 'testing', 'test_JobAdapter'),
@@ -224,7 +225,7 @@ def setUpClass(cls):
 
     def test_determine_job_array_parameters(self):
         """Test determining job array parameters"""
-        self.assertEqual(self.job_1.iterate_by, ['species', 'conformers'])
+        self.assertEqual(self.job_1.iterate_by, ['species', 'conf_opt'])
         self.assertEqual(self.job_1.number_of_processes, 3 * 6)
         self.assertEqual(self.job_1.workers, 4)
 

arc/job/adapters/cfour.py

@@ -212,12 +212,12 @@ def write_input_file(self) -> None:
         input_dict['keywords'] = ''
 
         keywords = list()
-        if self.job_type in ['opt', 'conformers']:
+        if self.job_type in ['opt', 'conf_opt']:
             keywords.append('METHOD=TS' if self.is_ts else 'METHOD=MANR')
         elif self.job_type in ['freq', 'optfreq', 'scan']:
             raise NotImplementedError(f'CFour cannot execute frequency computations or scans, '
                                       f'got job type {self.job_type}')
-        elif self.job_type == 'sp':
+        elif self.job_type in ['sp', 'conf_sp']:
             pass
 
         input_dict['keywords'] = ','.join(key for key in keywords)

arc/job/adapters/gaussian.py

@@ -275,7 +275,7 @@ def write_input_file(self) -> None:
                     max_c = int(match.group(1))
                     break
 
-        if self.job_type in ['opt', 'conformers', 'optfreq', 'composite']:
+        if self.job_type in ['opt', 'conf_opt', 'optfreq', 'composite']:
             keywords = ['ts', 'calcfc', 'noeigentest', f'maxcycle={max_c}'] if self.is_ts else ['calcfc']
             if self.level.method in ['rocbs-qb3']:
                 # There are no analytical 2nd derivatives (FC) for this method.
@@ -312,7 +312,7 @@ def write_input_file(self) -> None:
         elif self.job_type == 'optfreq':
             input_dict['job_type_2'] = 'freq IOp(7/33=1)'
 
-        elif self.job_type == 'sp':
+        elif self.job_type in ['sp', 'conf_sp']:
             input_dict['job_type_1'] = f'integral=(grid=ultrafine, {integral_algorithm})'
             if input_dict['trsh']:
                 input_dict['trsh'] += ' '

arc/job/adapters/molpro.py

@@ -233,15 +233,15 @@ def write_input_file(self) -> None:
             input_dict['restricted'] = 'u'
 
         # Job type specific options
-        if self.job_type in ['opt', 'optfreq', 'conformers']:
+        if self.job_type in ['opt', 'optfreq', 'conf_opt']:
             keywords = ['optg', 'root=2', 'method=qsd', 'readhess', "savexyz='geometry.xyz'"] if self.is_ts \
                 else ['optg', "savexyz='geometry.xyz'"]
             input_dict['job_type_1'] = ', '.join(key for key in keywords)
 
         elif self.job_type in ['freq', 'optfreq']:
             input_dict['job_type_2'] = '{frequencies;\nthermo;\nprint,HESSIAN,thermo;}'
 
-        elif self.job_type == 'sp':
+        elif self.job_type in ['sp', 'conf_sp']:
             pass
 
         elif self.job_type == 'scan':

arc/job/adapters/orca.py

@@ -259,7 +259,7 @@ def write_input_file(self) -> None:
         input_dict['restricted'] = 'r' if is_restricted(self) else 'u'
 
         # Job type specific options
-        if self.job_type in ['opt', 'conformers', 'optfreq']:
+        if self.job_type in ['opt', 'conf_opt', 'optfreq']:
             opt_convergence_key = 'fine_opt_convergence' if self.fine else 'opt_convergence'
             opt_convergence = self.args['keyword'].get(opt_convergence_key, '').lower() or \
                 orca_default_options_dict['opt']['keyword'].get(opt_convergence_key, '').lower()
@@ -293,7 +293,7 @@ def write_input_file(self) -> None:
                 logger.info('Using numerical frequencies calculation in Orca. Note: This job might therefore be '
                             'time-consuming.')
 
-        elif self.job_type == 'sp':
+        elif self.job_type in ['sp', 'conf_sp']:
             input_dict['job_type_1'] = 'sp'
 
         elif self.job_type == 'scan':

arc/job/adapters/qchem.py

@@ -224,7 +224,7 @@ def write_input_file(self) -> None:
         input_dict['unrestricted'] = 'True' if not is_restricted(self) else 'False'
 
         # Job type specific options
-        if self.job_type in ['opt', 'conformers', 'optfreq', 'orbitals', 'scan']:
+        if self.job_type in ['opt', 'conf_opt', 'optfreq', 'orbitals', 'scan']:
             input_dict['job_type_1'] = 'ts' if self.is_ts else 'opt'
             if self.fine:
                 input_dict['fine'] = '\n   GEOM_OPT_TOL_GRADIENT 15' \
@@ -238,7 +238,7 @@ def write_input_file(self) -> None:
         elif self.job_type == 'freq':
             input_dict['job_type_1'] = 'freq'
 
-        elif self.job_type == 'sp':
+        elif self.job_type in ['sp', 'conf_sp']:
             input_dict['job_type_1'] = 'sp'
 
         elif self.job_type == 'orbitals':

arc/job/adapters/scripts/ob_script.py

@@ -176,7 +176,7 @@ def main():
         sp = run_sp(mol=mol, ff_method=ff_method)
         save_output_file(path = str(args.yml_path), key="sp", val=sp)
 
-    if job_type in ['opt', 'conformers', 'directed_scan']:
+    if job_type in ['opt', 'conf_opt', 'directed_scan']:
         constraints = input_dict["constraints"] if "constraints" in input_dict.keys() else None
         opt_xyz, sp = constraint_opt(mol=mol, constraints_dict=constraints, ff_method=ff_method)
         save_output_file(path = str(args.yml_path), content_dict = {"opt_xyz" : opt_xyz, "sp" : sp})

arc/job/adapters/scripts/tani_script.py

@@ -345,7 +345,7 @@ def main():
         forces = run_force(xyz=xyz, device=device, model=model)
         save_output_file(path = str(args.yml_path), key="force", val=forces)
 
-    elif job_type in ['opt', 'conformers', 'directed_scan', 'optfreq']:
+    elif job_type in ['opt', 'conf_opt', 'directed_scan', 'optfreq']:
         constraints = input_dict["constraints"] if "constraints" in input_dict.keys() else None
         opt_xyz = run_opt(xyz=xyz, constraints=constraints, fmax=input_dict["fmax"], model=model,
                           steps=input_dict["steps"] if "steps" in input_dict.keys() else None, engine=input_dict["engine"])

arc/job/adapters/terachem.py

@@ -239,7 +239,7 @@ def write_input_file(self) -> None:
             input_dict['dispersion'] = 'no'
 
         # Job type specific options
-        if self.job_type in ['conformer', 'opt', 'scan']:
+        if self.job_type in ['conf_opt', 'opt', 'scan']:
             input_dict['job_type_1'] = 'minimize\n' \
                                        'new_minimizer yes'
             if self.fine:
@@ -250,7 +250,7 @@ def write_input_file(self) -> None:
         elif self.job_type == 'freq':
             input_dict['job_type_1'] = 'frequencies'
 
-        elif self.job_type == 'sp':
+        elif self.job_type in ['sp', 'conf_sp']:
             input_dict['job_type_1'] = 'energy'
 
         if self.job_type == 'scan' \

arc/job/adapters/xtb_adapter.py

@@ -216,7 +216,7 @@ def write_input_file(self) -> None:
         directives, block = '', ''
         uhf = self.species[0].number_of_radicals or self.multiplicity - 1
 
-        if self.job_type in ['opt', 'conformers', 'scan']:
+        if self.job_type in ['opt', 'conf_opt', 'scan']:
             directives += ' --opt'
             directives += self.add_accuracy()
             if self.constraints and self.job_type != 'scan':
@@ -234,7 +234,7 @@ def write_input_file(self) -> None:
         elif self.job_type in ['fukui']:
             directives += ' --vfukui'
 
-        elif self.job_type == 'sp':
+        elif self.job_type in ['sp', 'conf_sp']:
             pass
 
         directives += f' --{self.level.method}' if self.level is not None and self.level.method != 'xtb' else ' --gfn2'
@@ -291,7 +291,7 @@ def is_opt_ts_job(self) -> bool:
             bool: Whether this is a transition state geometry optimization job.
         """
         if self.species is not None and len(self.species) and self.species[0].is_ts \
-                and self.job_type in ['opt', 'conformers']:
+                and self.job_type in ['opt', 'conf_opt']:
             return True
         return False
 

arc/job/trsh.py

@@ -229,7 +229,7 @@ def determine_ess_status(output_path: str,
                     done = True
                     # If this is an opt job, we must also check that the max num of cycles hasn't been reached,
                     # so don't break yet.
-                    if 'opt' not in job_type and 'conformer' not in job_type and 'ts' not in job_type:
+                    if 'opt' not in job_type and 'conf_opt' not in job_type and 'ts' not in job_type:
                         break
                 elif 'SCF failed' in line:
                     keywords = ['SCF']
@@ -245,7 +245,7 @@ def determine_ess_status(output_path: str,
                 elif 'Invalid charge/multiplicity combination' in line:
                     raise SpeciesError(f'The multiplicity and charge combination for species '
                                        f'{species_label} are wrong.')
-                if 'opt' in job_type or 'conformer' in job_type or 'ts' in job_type:
+                if 'opt' in job_type or 'conf_opt' in job_type or 'ts' in job_type:
                     if 'MAXIMUM OPTIMIZATION CYCLES REACHED' in line:
                         keywords = ['MaxOptCycles']
                         error = 'Maximum optimization cycles reached.'