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updated mg model and fixes
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@carlocamilloni
carlocamilloni committed Jan 16, 2025
1 parent baa9531 commit d353b77
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Showing 3 changed files with 68 additions and 42 deletions.
22 changes: 7 additions & 15 deletions multi-ego-basic.ff/ffbonded.itp
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Original file line number Diff line number Diff line change
Expand Up @@ -64,7 +64,7 @@
#define gb_20 0.143500 2.500000e+05 ;
; CHn - OA (sugar) 600
;
#define gb_21 0.147000 1.000000e+05 ;
#define gb_21 0.147000 9.000000e+04 ;
; CHn - N, NT, NL, NZ, NE 900
;
#define gb_22 0.148000 2.500000e+05 ;
Expand Down Expand Up @@ -373,24 +373,18 @@
#define gd_41 0.000 3.77 6
; -CHn-NT- 0.9
;
; OPT November 23, 22
;#define gd_42 -170.0000 2.7000 2
#define gd_42 -170.0000 4.0000 2
#define gd_42 170.0000 2.5000 2
; Backbone dihedral angle -N-CA-C-N- ALA/SER/THR
;
;#define gd_43 15.0000 2.500 3
#define gd_43 15.0000 2.500 3
; Backbone dihedral angle -C-N-CA-C- ALA/SER/THR
;
;#define gd_44 180.000 4.00 1
#define gd_44 165.000 4.00 1
#define gd_44 180.000 5.50 1
; Backbone dihedral angle -C-N-CA-C- ALA/SER/THR
;
;#define gd_45 -105.00 0.2200 1
#define gd_45 -180.00 0.40 1
#define gd_45 180.00 0.20 1
; Backbone dihedral angle -N-CA-C-N- ALA/SER/THR
;
; OPT November 24, 22
#define gd_42b 130.0000 2.5000 2
; Backbone dihedral angle -N-CA-C-N- BULKYAA
;
Expand All @@ -400,27 +394,25 @@
#define gd_44b -110.000 3.90 1
; Backbone dihedral angle -C-N-CA-C- BULKYAA
;
#define gd_45b 180.000 0.80 1
#define gd_45b 180.000 0.20 1
; Backbone dihedral angle -N-CA-C-N- BULKYAA
;
; OPT April, 3 2024
#define gd_48 180.0 5 2
; Backbone dihedral angle -N-CA-C-N GLY
;
#define gd_49 180 11 1
; Backbone dihedral angle -C-N-CA-C GLY
;
#define gd_51 -180 2.5 1
#define gd_51 -180 1.8 1
; Backbone dihedral angle -N-CA-C-N GLY
;
; OPT November, 23 2022
#define gd_52 150.0000 4.0000 2
; Backbone dihedral angle -N-CA-C-N PRO
;
#define gd_53 0.0000 4.000 4
; Backbone dihedral angle -C-N-CA-C PRO
;
#define gd_55 -60.0000 0.400 1
#define gd_55 -60.0000 1.200 1
; Backbone dihedral angle -N-CA-C-N PRO
;
#define gd_56 180.000 31.0 2
Expand Down
87 changes: 60 additions & 27 deletions src/multiego/ensemble.py
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Original file line number Diff line number Diff line change
Expand Up @@ -784,19 +784,33 @@ def init_LJ_datasets(meGO_ensemble, matrices, pairs14, exclusion_bonds14, args):
symmetrize=True,
)

pairwise_c12 = np.where(
oxygen_mask,
3e-6,
ho_mask = masking.create_linearized_mask(
type_ai_mapped.to_numpy(),
type_aj_mapped.to_numpy(),
[("H", "O"), ("H", "OM")],
symmetrize=True,
)

# Define condition where only ai or aj (but not both) starts with "H"
h_condition = (
train_dataset["ai"].str.startswith("H") ^ train_dataset["aj"].str.startswith("H")
)

hh_condition = (
train_dataset["ai"].str.startswith("H") & train_dataset["aj"].str.startswith("H")
)

pairwise_c12 = (
np.sqrt(
train_dataset["ai"].map(meGO_ensemble["sbtype_c12_dict"])
* train_dataset["aj"].map(meGO_ensemble["sbtype_c12_dict"])
),
)
)
train_dataset["rep"] = train_dataset["rep"].fillna(pd.Series(pairwise_c12))
train_dataset.loc[oxygen_mask, "rep"] = 3e-6
print(train_dataset.to_string())

pairwise_mg_sigma = np.where(
oxygen_mask,
(3e-6) ** (1 / 12),
pairwise_mg_sigma = (
(
train_dataset["ai"].map(meGO_ensemble["sbtype_mg_c12_dict"])
* train_dataset["aj"].map(meGO_ensemble["sbtype_mg_c12_dict"])
Expand All @@ -805,26 +819,37 @@ def init_LJ_datasets(meGO_ensemble, matrices, pairs14, exclusion_bonds14, args):
* train_dataset["aj"].map(meGO_ensemble["sbtype_mg_c6_dict"])
)
)
** (1 / 12),
** (1 / 12)
)
train_dataset["mg_sigma"] = pd.Series(pairwise_mg_sigma)

pairwise_mg_epsilon = np.where(
oxygen_mask,
-3e-6,
(
train_dataset["ai"].map(meGO_ensemble["sbtype_mg_c6_dict"])
* train_dataset["aj"].map(meGO_ensemble["sbtype_mg_c6_dict"])
train_dataset.loc[oxygen_mask, "mg_sigma"] = (3e-6) ** (1 / 12)
# Apply the specific value for this condition
#train_dataset.loc[h_condition, "mg_sigma"] = 0.
train_dataset.loc[hh_condition, "mg_sigma"] = train_dataset["rep"] ** (1 /12)
train_dataset.loc[ho_mask, "mg_sigma"] = 0.169500

# Generate the default pairwise_mg_epsilon
pairwise_mg_epsilon = (
train_dataset["ai"].map(meGO_ensemble["sbtype_mg_c6_dict"])
* train_dataset["aj"].map(meGO_ensemble["sbtype_mg_c6_dict"])
) / (
4
* np.sqrt(
train_dataset["ai"].map(meGO_ensemble["sbtype_mg_c12_dict"])
* train_dataset["aj"].map(meGO_ensemble["sbtype_mg_c12_dict"])
)
/ (
4
* np.sqrt(
train_dataset["ai"].map(meGO_ensemble["sbtype_mg_c12_dict"])
* train_dataset["aj"].map(meGO_ensemble["sbtype_mg_c12_dict"])
)
),
)

# Initialize pairwise_mg_epsilon with default values
train_dataset["mg_epsilon"] = pd.Series(pairwise_mg_epsilon)
train_dataset.loc[oxygen_mask, "mg_epsilon"] = -3e-6

# Apply the specific value for this condition
train_dataset.loc[h_condition, "mg_epsilon"] = 0.0
train_dataset.loc[hh_condition, "mg_epsilon"] = -train_dataset["rep"]
train_dataset.loc[ho_mask, "mg_epsilon"] = 0.11

train_dataset.dropna(subset=["mg_sigma"], inplace=True)

return train_dataset

Expand All @@ -839,7 +864,7 @@ def generate_OO_LJ(meGO_ensemble):
]
H_H_sbtype = [sbtype for sbtype, atomtype in meGO_ensemble["sbtype_type_dict"].items() if atomtype == "H"]

full_matrix = list(itertools.product(H_H_sbtype, O_OM_sbtype)) + list(itertools.product(O_OM_sbtype, H_H_sbtype))
full_matrix_OH = list(itertools.product(H_H_sbtype, O_OM_sbtype)) + list(itertools.product(O_OM_sbtype, H_H_sbtype))

# Generate all possible combinations
combinations = list(itertools.product(O_OM_sbtype, repeat=2))
Expand All @@ -861,9 +886,7 @@ def generate_OO_LJ(meGO_ensemble):
HH_LJ["sigma"] = HH_LJ["c12"] ** (1.0 / 12.0)
HH_LJ["mg_sigma"] = HH_LJ["c12"] ** (1 / 12)
HH_LJ["mg_epsilon"] = -HH_LJ["c12"]
HO_LJ = pd.DataFrame(full_matrix, columns=["ai", "aj"])
# HO_LJ["c12"] = 1.153070e-08 * type_definitions.mg_eps
# HO_LJ["c6"] = 2.147622e-04 * type_definitions.mg_eps
HO_LJ = pd.DataFrame(full_matrix_OH, columns=["ai", "aj"])
HO_LJ["c12"] = 2.249554e-09 * type_definitions.mg_eps
HO_LJ["c6"] = 9.485893e-05 * type_definitions.mg_eps
HO_LJ["epsilon"] = type_definitions.mg_eps
Expand Down Expand Up @@ -1548,8 +1571,18 @@ def make_pairs_exclusion_topology(meGO_ensemble, meGO_LJ_14, args):
filtered_combinations.append((sbtype_with_residue[i][0], sbtype_with_residue[j][0]))
j += 1

# Filter out invalid combinations
valid_combinations = [
(ai, aj)
for ai, aj in filtered_combinations
if not (
(meGO_ensemble["sbtype_type_dict"][ai] == "H" and meGO_ensemble["sbtype_type_dict"][aj] not in {"H", "O", "OM"})
or (meGO_ensemble["sbtype_type_dict"][aj] == "H" and meGO_ensemble["sbtype_type_dict"][ai] not in {"H", "O", "OM"})
)
]

# Create a DataFrame from the filtered combinations
df = pd.DataFrame(filtered_combinations, columns=["ai", "aj"])
df = pd.DataFrame(valid_combinations, columns=["ai", "aj"])
df["c6"] = 0.0
df["c12"] = np.sqrt(
df["ai"].map(meGO_ensemble["sbtype_c12_dict"]) * df["aj"].map(meGO_ensemble["sbtype_c12_dict"])
Expand Down
1 change: 1 addition & 0 deletions src/multiego/resources/type_definitions.py
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Original file line number Diff line number Diff line change
Expand Up @@ -120,6 +120,7 @@

# Dictionary mapping atom types from a force field to multiego representation
from_ff_to_multiego = {
"HN": "H",
"OC1": "O1",
"OC2": "O2",
"OT1": "O1",
Expand Down

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