MLIP-Studio/model_config.py at main · mlipstudio/MLIP-Studio · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
from orb_models.forcefield import pretrained
MACE_MODELS = {
    "MACE MPA Medium": "https://github.com/ACEsuit/mace-mp/releases/download/mace_mpa_0/mace-mpa-0-medium.model",
    "MACE OMAT Medium": "https://github.com/ACEsuit/mace-mp/releases/download/mace_omat_0/mace-omat-0-medium.model",
    "MACE OMAT Small": "https://github.com/ACEsuit/mace-mp/releases/download/mace_omat_0/mace-omat-0-small.model",
    "MACE MATPES r2SCAN Medium": "https://github.com/ACEsuit/mace-foundations/releases/download/mace_matpes_0/MACE-matpes-r2scan-omat-ft.model",
    "MACE MATPES PBE Medium": "https://github.com/ACEsuit/mace-foundations/releases/download/mace_matpes_0/MACE-matpes-pbe-omat-ft.model",
    "MACE MP 0a Small": "https://github.com/ACEsuit/mace-mp/releases/download/mace_mp_0/2023-12-10-mace-128-L0_energy_epoch-249.model",
    "MACE MP 0a Medium": "https://github.com/ACEsuit/mace-mp/releases/download/mace_mp_0/2023-12-03-mace-128-L1_epoch-199.model",
    "MACE MP 0a Large": "https://github.com/ACEsuit/mace-mp/releases/download/mace_mp_0/2024-01-07-mace-128-L2_epoch-199.model",
    "MACE MP 0b Small": "https://github.com/ACEsuit/mace-foundations/releases/download/mace_mp_0b/mace_agnesi_small.model",
    "MACE MP 0b Medium": "https://github.com/ACEsuit/mace-foundations/releases/download/mace_mp_0b/mace_agnesi_medium.model",
    "MACE MP 0b2 Small": "https://github.com/ACEsuit/mace-foundations/releases/download/mace_mp_0b2/mace-small-density-agnesi-stress.model", # Corrected name from original code
    "MACE MP 0b2 Medium": "https://github.com/ACEsuit/mace-foundations/releases/download/mace_mp_0b2/mace-medium-density-agnesi-stress.model",
    "MACE MP 0b2 Large": "https://github.com/ACEsuit/mace-foundations/releases/download/mace_mp_0b2/mace-large-density-agnesi-stress.model",
    "MACE MP 0b3 Medium": "https://github.com/ACEsuit/mace-foundations/releases/download/mace_mp_0b3/mace-mp-0b3-medium.model",
    "MACE ANI-CC Large (500k)": "https://github.com/ACEsuit/mace/raw/main/mace/calculators/foundations_models/ani500k_large_CC.model",
    "MACE OMOL-0 XL 4M": "https://github.com/ACEsuit/mace-foundations/releases/download/mace_omol_0/mace-omol-0-extra-large-4M.model",
    "MACE OMOL-0 XL 1024": "https://github.com/ACEsuit/mace-foundations/releases/download/mace_omol_0/MACE-omol-0-extra-large-1024.model",
    "MACE OFF 23 Large": "https://github.com/ACEsuit/mace-off/raw/main/mace_off23/MACE-OFF23_large.model",
    "MACE OFF 23 Medium": "https://github.com/ACEsuit/mace-off/raw/main/mace_off23/MACE-OFF23_medium.model",
    "MACE OFF 23 Small": "https://github.com/ACEsuit/mace-off/raw/main/mace_off23/MACE-OFF23_small.model",
    "MACE OFF 24 Medium": "https://github.com/ACEsuit/mace-off/raw/main/mace_off24/MACE-OFF24_medium.model",
    "MACE POLAR 1 S": "https://github.com/ACEsuit/mace-foundations/releases/download/mace_polar_1/MACE-POLAR-1-S.model",
    "MACE POLAR 1 M": "https://github.com/ACEsuit/mace-foundations/releases/download/mace_polar_1/MACE-POLAR-1-M.model",
    "MACE POLAR 1 L": "https://github.com/ACEsuit/mace-foundations/releases/download/mace_polar_1/MACE-POLAR-1-L.model"
}
MACE_CITATIONS = {
    # --- MACE-MP (Materials Project) Models ---
    "MACE MP 0a Small": "**Model:** Batatia et al., *J. Chem. Phys.* 163, 184110 (2025) [arXiv:2312.15211]  \n**Data:** Jain et al., *APL Mater.* 1, 011002 (2013) (Materials Project)",
    "MACE MP 0a Medium": "**Model:** Batatia et al., *J. Chem. Phys.* 163, 184110 (2025) [arXiv:2312.15211]  \n**Data:** Jain et al., *APL Mater.* 1, 011002 (2013) (Materials Project)",
    "MACE MP 0a Large": "**Model:** Batatia et al., *J. Chem. Phys.* 163, 184110 (2025) [arXiv:2312.15211]  \n**Data:** Jain et al., *APL Mater.* 1, 011002 (2013) (Materials Project)",
    "MACE MP 0b Small": "**Model:** Batatia et al., *J. Chem. Phys.* 163, 184110 (2025) [arXiv:2312.15211]  \n**Data:** Jain et al., *APL Mater.* 1, 011002 (2013) (Materials Project)",
    "MACE MP 0b Medium": "**Model:** Batatia et al., *J. Chem. Phys.* 163, 184110 (2025) [arXiv:2312.15211]  \n**Data:** Jain et al., *APL Mater.* 1, 011002 (2013) (Materials Project)",
    "MACE MP 0b2 Small": "**Model:** Batatia et al., *J. Chem. Phys.* 163, 184110 (2025) [arXiv:2312.15211]  \n**Data:** Jain et al., *APL Mater.* 1, 011002 (2013) (Materials Project)",
    "MACE MP 0b2 Medium": "**Model:** Batatia et al., *J. Chem. Phys.* 163, 184110 (2025) [arXiv:2312.15211]  \n**Data:** Jain et al., *APL Mater.* 1, 011002 (2013) (Materials Project)",
    "MACE MP 0b2 Large": "**Model:** Batatia et al., *J. Chem. Phys.* 163, 184110 (2025) [arXiv:2312.15211]  \n**Data:** Jain et al., *APL Mater.* 1, 011002 (2013) (Materials Project)",
    "MACE MP 0b3 Medium": "**Model:** Batatia et al., *J. Chem. Phys.* 163, 184110 (2025) [arXiv:2312.15211]  \n**Data:** Jain et al., *APL Mater.* 1, 011002 (2013) (Materials Project)",

    # --- MACE-MPA (Materials Project Augmented) ---
    "MACE MPA Medium": "**Model:** Batatia et al., *J. Chem. Phys.* 163, 184110 (2025)  \n**Data:** Jain et al., *APL Mater.* 1, 011002 (2013) (Materials Project)",

    # --- MACE-OMAT (Open Materials) ---
    "MACE OMAT Medium": "**Model:** Batatia et al., *arXiv:2510.25380* (2025) (Cross Learning/OMAT)  \n**Data:** OMat24 Dataset (Meta FAIR), *arXiv:2410.12771* (2024)",
    "MACE OMAT Small": "**Model:** Batatia et al., *arXiv:2510.25380* (2025) (Cross Learning/OMAT)  \n**Data:** OMat24 Dataset (Meta FAIR), *arXiv:2410.12771* (2024)",

    # --- MACE-OMOL (Open Molecules) ---
    "MACE OMOL-0 XL 4M": "**Model:** Batatia et al., *arXiv:2510.24063* (2025) (MACE-OMol-0)  \n**Data:** OMol24/25 Dataset (Meta FAIR)",
    "MACE OMOL-0 XL 1024": "**Model:** Batatia et al., *arXiv:2510.24063* (2025) (MACE-OMol-0)  \n**Data:** OMol24/25 Dataset (Meta FAIR)",

    # --- MACE-MATPES (PES Finetuned) ---
    "MACE MATPES r2SCAN Medium": "**Model:** Batatia et al., *J. Chem. Phys.* 163, 184110 (2025)  \n**Data:** MatPES/MP-ALOE (r2SCAN), Kuner et al., *npj Comput. Mater.* 11, 1 (2025)",
    "MACE MATPES PBE Medium": "**Model:** Batatia et al., *J. Chem. Phys.* 163, 184110 (2025)  \n**Data:** MatPES/Materials Project (PBE)",

    # --- MACE-OFF (Open Force Field) ---
    "MACE OFF 23 Small": "**Model:** Kovács et al., *J. Chem. Theory Comput.* (2024) [arXiv:2312.15211]  \n**Data:** Eastman et al., *J. Chem. Theory Comput.* 19, 209 (2023) (SPICE)",
    "MACE OFF 23 Medium": "**Model:** Kovács et al., *J. Chem. Theory Comput.* (2024) [arXiv:2312.15211]  \n**Data:** Eastman et al., *J. Chem. Theory Comput.* 19, 209 (2023) (SPICE)",
    "MACE OFF 23 Large": "**Model:** Kovács et al., *J. Chem. Theory Comput.* (2024) [arXiv:2312.15211]  \n**Data:** Eastman et al., *J. Chem. Theory Comput.* 19, 209 (2023) (SPICE)",
    "MACE OFF 24 Medium": "**Model:** Kovács et al., *arXiv:2312.15211* (updated 2024)  \n**Data:** Eastman et al., *J. Chem. Theory Comput.* 19, 209 (2023) (SPICE 2.0)",

    # --- MACE ANI-CC ---
    "MACE ANI-CC Large (500k)": "**Model:** Batatia et al., *NeurIPS* (2022) (MACE Architecture)  \n**Data:** Smith et al., *Nat. Commun.* 11, 2965 (2020) (ANI-1ccx)",
    # --- MACE POLAR ---
    "MACE POLAR 1 S": "Batatia, Ilyes, et al. *MACE-POLAR-1: A Polarisable Electrostatic Foundation Model for Molecular Chemistry.* arXiv preprint arXiv:2602.19411 (2026).",
    "MACE POLAR 1 M": "Batatia, Ilyes, et al. *MACE-POLAR-1: A Polarisable Electrostatic Foundation Model for Molecular Chemistry.* arXiv preprint arXiv:2602.19411 (2026).",
    "MACE POLAR 1 L": "Batatia, Ilyes, et al. *MACE-POLAR-1: A Polarisable Electrostatic Foundation Model for Molecular Chemistry.* arXiv preprint arXiv:2602.19411 (2026)."
}

FAIRCHEM_MODELS = {
    "UMA Small 1.2": "uma-s-1p2",
    "UMA Small 1.1": "uma-s-1p1",
    # "UMA Small 1": "uma-s-1", # No longer available
    "ESEN MD Direct All OMOL": "esen-md-direct-all-omol",
    "ESEN SM Conserving All OMOL": "esen-sm-conserving-all-omol",
    "ESEN SM Direct All OMOL": "esen-sm-direct-all-omol"
}
FAIRCHEM_CITATIONS = {
    "UMA Small 1.2": "Wood, Brandon M., et al. *Uma: A family of universal models for atoms.* arXiv preprint arXiv:2506.23971 (2025).",
    "UMA Small 1.1": "Wood, Brandon M., et al. *Uma: A family of universal models for atoms.* arXiv preprint arXiv:2506.23971 (2025).",
    # "UMA Small 1": "uma-s-1", # No longer available
    "ESEN MD Direct All OMOL": "Fu, Xiang, et al. *Learning smooth and expressive interatomic potentials for physical property prediction.* arXiv preprint arXiv:2502.12147 (2025).",
    "ESEN SM Conserving All OMOL": "Fu, Xiang, et al. *Learning smooth and expressive interatomic potentials for physical property prediction.* arXiv preprint arXiv:2502.12147 (2025).",
    "ESEN SM Direct All OMOL": "Fu, Xiang, et al. *Learning smooth and expressive interatomic potentials for physical property prediction.* arXiv preprint arXiv:2502.12147 (2025)."
}
# Define the available ORB models
ORB_MODELS = {
    "V3 OMOL Conservative": pretrained.orb_v3_conservative_omol,
    "V3 OMOL Direct": pretrained.orb_v3_direct_omol,
    "V3 OMAT Conservative (inf)": pretrained.orb_v3_conservative_inf_omat,
    "V3 OMAT Conservative (20)": pretrained.orb_v3_conservative_20_omat,
    "V3 OMAT Direct (inf)": pretrained.orb_v3_direct_inf_omat,
    "V3 OMAT Direct (20)": pretrained.orb_v3_direct_20_omat,
    "V3 MPA Conservative (inf)": pretrained.orb_v3_conservative_inf_mpa,
    "V3 MPA Conservative (20)": pretrained.orb_v3_conservative_20_mpa,
    "V3 MPA Direct (inf)": pretrained.orb_v3_direct_inf_mpa,
    "V3 MPA Direct (20)": pretrained.orb_v3_direct_20_mpa,
}
ORB_CITATIONS = {
    "V3 OMOL Conservative": "Rhodes, Benjamin, et al. *Orb-v3: atomistic simulation at scale.* arXiv preprint arXiv:2504.06231 (2025).",
    "V3 OMOL Direct": "Rhodes, Benjamin, et al. *Orb-v3: atomistic simulation at scale.* arXiv preprint arXiv:2504.06231 (2025).",
    "V3 OMAT Conservative (inf)": "Rhodes, Benjamin, et al. *Orb-v3: atomistic simulation at scale.* arXiv preprint arXiv:2504.06231 (2025).",
    "V3 OMAT Conservative (20)": "Rhodes, Benjamin, et al. *Orb-v3: atomistic simulation at scale.* arXiv preprint arXiv:2504.06231 (2025).",
    "V3 OMAT Direct (inf)": "Rhodes, Benjamin, et al. *Orb-v3: atomistic simulation at scale.* arXiv preprint arXiv:2504.06231 (2025).",
    "V3 OMAT Direct (20)": "Rhodes, Benjamin, et al. *Orb-v3: atomistic simulation at scale.* arXiv preprint arXiv:2504.06231 (2025).",
    "V3 MPA Conservative (inf)": "Rhodes, Benjamin, et al. *Orb-v3: atomistic simulation at scale.* arXiv preprint arXiv:2504.06231 (2025).",
    "V3 MPA Conservative (20)": "Rhodes, Benjamin, et al. *Orb-v3: atomistic simulation at scale.* arXiv preprint arXiv:2504.06231 (2025).",
    "V3 MPA Direct (inf)": "Rhodes, Benjamin, et al. *Orb-v3: atomistic simulation at scale.* arXiv preprint arXiv:2504.06231 (2025).",
    "V3 MPA Direct (20)": "Rhodes, Benjamin, et al. *Orb-v3: atomistic simulation at scale.* arXiv preprint arXiv:2504.06231 (2025).",
}
# Define the available MatterSim models
MATTERSIM_MODELS = {
    "V1 SMALL": "MatterSim-v1.0.0-1M.pth",
    "V1 LARGE": "MatterSim-v1.0.0-5M.pth"
}
MATTERSIM_CITATIONS = {
    "V1 SMALL": "Yang, Han, et al. *Mattersim: A deep learning atomistic model across elements, temperatures and pressures.* arXiv preprint arXiv:2405.04967 (2024).",
    "V1 LARGE": "Yang, Han, et al. *Mattersim: A deep learning atomistic model across elements, temperatures and pressures.* arXiv preprint arXiv:2405.04967 (2024)."
}
# Define the available UPET models
UPET_MODELS = {
    # PET-MAD - materials and molecules
    "PET-MAD-XS-V1.5.0": "pet-mad-xs",
    "PET-MAD-S-V1.5.0": "pet-mad-s",
    "PET-MAD-S-V1.1.0": "pet-mad-s",
    "PET-MAD-S-V1.0.2": "pet-mad-s",

    # PET-OAM (PBE Materials Project) - materials
    "PET-OAM-L-V0.1.0": "pet-oam-l",
    # "PET-OAM-XL-V0.1.0": "pet-oam-xl",


    # PET-OMat (PBE) - materials
    "PET-OMAT-XS-V1.0.0": "pet-omat-xs",
    "PET-OMAT-S-V1.0.0": "pet-omat-s",
    "PET-OMAT-M-V1.0.0": "pet-omat-m",
    "PET-OMAT-L-V1.0.0": "pet-omat-l",
    # "PET-OMAT-XL-V1.0.0": "pet-omat-xl",

    # PET-OMATPES (r2SCAN) - materials
    "PET-OMATPES-L-V0.1.0": "pet-omatpes-l",

    # PET-SPICE (wB97M-D3) - molecules
    "PET-SPICE-S-V0.2.0": "pet-spice-s",
    "PET-SPICE-L-V0.2.0": "pet-spice-l",

    "PET-MAD-DOS": "pet-mad-dos",

    "PET-OMAD-XS-V1.0.0": "pet-omad-xs",
    "PET-OMAD-S-V1.0.0": "pet-omad-s",
    "PET-OMAD-L-V0.1.0": "pet-omad-l",
}

UPET_MODELS_VERSIONS = {
    # PET-MAD - materials and molecules
    "PET-MAD-XS-V1.5.0": "1.5.0",
    "PET-MAD-S-V1.5.0": "1.5.0",
    "PET-MAD-S-V1.1.0": "1.5.0",
    "PET-MAD-S-V1.0.2": "1.0.2",

    # PET-OAM (PBE Materials Project) - materials
    "PET-OAM-L-V0.1.0": "0.1.0",
    "PET-OAM-XL-V0.1.0": "0.1.0",


    # PET-OMat (PBE) - materials
    "PET-OMAT-XS-V1.0.0": "1.0.0",
    "PET-OMAT-S-V1.0.0": "1.0.0",
    "PET-OMAT-M-V1.0.0": "1.0.0",
    "PET-OMAT-L-V1.0.0": "1.0.0",
    "PET-OMAT-XL-V1.0.0": "1.0.0",

    # PET-OMATPES (r2SCAN) - materials
    "PET-OMATPES-L-V0.1.0": "0.1.0",

    # PET-SPICE (wB97M-D3) - molecules
    "PET-SPICE-S-V0.2.0": "0.2.0",
    "PET-SPICE-L-V0.2.0": "0.2.0",

    "PET-MAD-DOS": "pet-mad-dos",

    "PET-OMAD-XS-V1.0.0": "1.0.0",
    "PET-OMAD-S-V1.0.0": "1.0.0",
    "PET-OMAD-L-V0.1.0": "0.1.0",
}
UPET_CITATIONS = {
    # PET-MAD - materials and molecules
    "PET-MAD-XS-V1.5.0": "Malosso, Cesare, et al. *High-quality, high-information datasets for universal atomistic machine learning.* arXiv preprint arXiv:2603.02089 (2026).",
    "PET-MAD-S-V1.5.0": "Malosso, Cesare, et al. *High-quality, high-information datasets for universal atomistic machine learning.* arXiv preprint arXiv:2603.02089 (2026).",
    "PET-MAD-S-V1.1.0": "Mazitov, Arslan, et al. *PET-MAD as a lightweight universal interatomic potential for advanced materials modeling.* Nature Communications 16.1 (2025): 10653.",
    "PET-MAD-S-V1.0.2": "Mazitov, Arslan, et al. *PET-MAD as a lightweight universal interatomic potential for advanced materials modeling.* Nature Communications 16.1 (2025): 10653.",

    # PET-OAM (PBE Materials Project) - materials
    "PET-OAM-L-V0.1.0": "Mazitov, Arslan, et al. *PET-MAD as a lightweight universal interatomic potential for advanced materials modeling.* Nature Communications 16.1 (2025): 10653.",
    # "PET-OAM-XL-V0.1.0": "pet-oam-xl",


    # PET-OMat (PBE) - materials
    "PET-OMAT-XS-V1.0.0": "Mazitov, Arslan, et al. *PET-MAD as a lightweight universal interatomic potential for advanced materials modeling.* Nature Communications 16.1 (2025): 10653.",
    "PET-OMAT-S-V1.0.0": "Mazitov, Arslan, et al. *PET-MAD as a lightweight universal interatomic potential for advanced materials modeling.* Nature Communications 16.1 (2025): 10653.",
    "PET-OMAT-M-V1.0.0": "Mazitov, Arslan, et al. *PET-MAD as a lightweight universal interatomic potential for advanced materials modeling.* Nature Communications 16.1 (2025): 10653.",
    "PET-OMAT-L-V1.0.0": "Mazitov, Arslan, et al. *PET-MAD as a lightweight universal interatomic potential for advanced materials modeling.* Nature Communications 16.1 (2025): 10653.",
    # "PET-OMAT-XL-V1.0.0": "pet-omat-xl",

    # PET-OMATPES (r2SCAN) - materials
    "PET-OMATPES-L-V0.1.0": "Mazitov, Arslan, et al. *PET-MAD as a lightweight universal interatomic potential for advanced materials modeling.* Nature Communications 16.1 (2025): 10653.",

    # PET-SPICE (wB97M-D3) - molecules
    "PET-SPICE-S-V0.2.0": "Mazitov, Arslan, et al. *PET-MAD as a lightweight universal interatomic potential for advanced materials modeling.* Nature Communications 16.1 (2025): 10653.",
    "PET-SPICE-L-V0.2.0": "Mazitov, Arslan, et al. *PET-MAD as a lightweight universal interatomic potential for advanced materials modeling.* Nature Communications 16.1 (2025): 10653.",

    "PET-MAD-DOS": "How, Wei Bin, et al. *A universal machine learning model for the electronic density of states.* Digital Discovery (2026).",

    "PET-OMAD-XS-V1.0.0": "Mazitov, Arslan, et al. *PET-MAD as a lightweight universal interatomic potential for advanced materials modeling.* Nature Communications 16.1 (2025): 10653.",
    "PET-OMAD-S-V1.0.0": "Mazitov, Arslan, et al. *PET-MAD as a lightweight universal interatomic potential for advanced materials modeling.* Nature Communications 16.1 (2025): 10653.",
    "PET-OMAD-L-V0.1.0": "Mazitov, Arslan, et al. *PET-MAD as a lightweight universal interatomic potential for advanced materials modeling.* Nature Communications 16.1 (2025): 10653.",
}
SEVEN_NET_MODELS = {
    "7net-0": "7net-0",
    "7net-l3i5": "7net-l3i5",
    "7net-omat": "7net-omat",
    "7net-mf-ompa": "7net-mf-ompa",
    "7net-omni": "7net-omni",
    # "7net-omni-i8": "7net-omni-i8",
    # "7net-omni-i12": "7net-omni-i12",
}
SEVEN_NET_CITATIONS = {
    "7net-0": "Park, Yutack, et al. *Scalable parallel algorithm for graph neural network interatomic potentials in molecular dynamics simulations.* Journal of chemical theory and computation 20.11 (2024): 4857-4868.",
    "7net-l3i5": "Park, Yutack, et al. *Scalable parallel algorithm for graph neural network interatomic potentials in molecular dynamics simulations.* Journal of chemical theory and computation 20.11 (2024): 4857-4868.",
    "7net-omat": "Park, Yutack, et al. *Scalable parallel algorithm for graph neural network interatomic potentials in molecular dynamics simulations.* Journal of chemical theory and computation 20.11 (2024): 4857-4868.",
    "7net-mf-ompa": "Kim, Jaesun, et al. *Data-efficient multifidelity training for high-fidelity machine learning interatomic potentials.* Journal of the American Chemical Society 147.1 (2024): 1042-1054.",
    "7net-omni": "Kim, Jaesun, et al. *Optimizing cross-domain transfer for universal machine learning interatomic potentials.* Nature Communications (2026).",
    # "7net-omni-i8": "Kim, Jaesun, et al. *Optimizing cross-domain transfer for universal machine learning interatomic potentials.* Nature Communications (2026).",
    # "7net-omni-i12": "Kim, Jaesun, et al. *Optimizing cross-domain transfer for universal machine learning interatomic potentials.* Nature Communications (2026).",
}


# Dictionary of sample structures
SAMPLE_STRUCTURES = {
    "Water": "H2O.xyz",
    "Methane": "CH4.xyz",
    "Ethane": "C2H6.xyz",
    "Benzene": "C6H6.xyz",
    "Fulvene": "Fulvene.xyz",
    "Caffeine": "caffeine.xyz",
    "Ibuprofen": "ibuprofen.xyz",
    "C60": "C60.xyz",
    "Aspirin": "aspirin.xyz",
    "Taxol": "Taxol.xyz",
    "Valinomycin": "Valinomycin.xyz",
    "Olestra": "Olestra.xyz",
    "Ubiquitin": "Ubiquitin.xyz",
    "Silicon": "Si.cif",
    "Copper": "Cu.cif",
    "Molybdenum": "Mo.cif",
    "Al2O3 (bulk)": "Al2O3.cif",
    "MoS2 (bulk)": "MoS2.cif",
    "MoSe2 (bulk)": "MoSe2.cif",
    "Liquid water 64 (bulk)": "water_64.extxyz",
    "Al2O3 (0001) Surface": "Al2O3_0001.cif",
    "hBN Monolayer (4x4)": "hBN_monolayer_4x4_supercell.extxyz",
    "Graphene Monolayer (4x4)": "graphene_monolayer_4x4_supercell.extxyz",
    "Cu(111) Surface": "Cu111_slab.cif",
    "CO on Cu(111)": "CO_on_Cu111.xyz",
}