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1 Parent(s): af4a473

add combustion page

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Files changed (9) hide show
  1. mlip_arena/models/registry.yaml +4 -0
  2. mlip_arena/tasks/combustion/chgnet/hydrogen.json +1 -0
  3. mlip_arena/tasks/combustion/equiformer/hydrogen.json +1 -0
  4. mlip_arena/tasks/combustion/escn/hydrogen.json +1 -0
  5. mlip_arena/tasks/combustion/mace-mp/hydrogen.json +1 -0
  6. mlip_arena/tasks/run.py +299 -0
  7. serve/app.py +6 -6
  8. serve/models/leaderboard.py +8 -7
  9. serve/tasks/combustion.py +132 -0
mlip_arena/models/registry.yaml CHANGED
@@ -13,6 +13,7 @@ MACE-MP(M):
13
  gpu-tasks:
14
  - homonuclear-diatomics
15
  - stability
 
16
  github: https://github.com/ACEsuit/mace
17
  doi: https://arxiv.org/abs/2401.00096
18
  date: 2023-12-29
@@ -32,6 +33,7 @@ CHGNet:
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  gpu-tasks:
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  - homonuclear-diatomics
34
  - stability
 
35
  prediction: EFSM
36
  nvt: true
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  npt: true
@@ -47,6 +49,7 @@ EquiformerV2(OC22):
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  - ocp
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  gpu-tasks:
49
  - homonuclear-diatomics
 
50
  github: https://github.com/FAIR-Chem/fairchem
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  doi: https://arxiv.org/abs/2306.12059
52
  prediction: EF
@@ -64,6 +67,7 @@ eSCN(OC20):
64
  - ocp
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  gpu-tasks:
66
  - homonuclear-diatomics
 
67
  github: https://github.com/FAIR-Chem/fairchem
68
  doi: https://arxiv.org/abs/2302.03655
69
  prediction: EF
 
13
  gpu-tasks:
14
  - homonuclear-diatomics
15
  - stability
16
+ - combustion
17
  github: https://github.com/ACEsuit/mace
18
  doi: https://arxiv.org/abs/2401.00096
19
  date: 2023-12-29
 
33
  gpu-tasks:
34
  - homonuclear-diatomics
35
  - stability
36
+ - combustion
37
  prediction: EFSM
38
  nvt: true
39
  npt: true
 
49
  - ocp
50
  gpu-tasks:
51
  - homonuclear-diatomics
52
+ - combustion
53
  github: https://github.com/FAIR-Chem/fairchem
54
  doi: https://arxiv.org/abs/2306.12059
55
  prediction: EF
 
67
  - ocp
68
  gpu-tasks:
69
  - homonuclear-diatomics
70
+ - combustion
71
  github: https://github.com/FAIR-Chem/fairchem
72
  doi: https://arxiv.org/abs/2302.03655
73
  prediction: EF
mlip_arena/tasks/combustion/chgnet/hydrogen.json ADDED
@@ -0,0 +1 @@
 
 
1
+ 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mlip_arena/tasks/combustion/equiformer/hydrogen.json ADDED
@@ -0,0 +1 @@
 
 
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mlip_arena/tasks/combustion/escn/hydrogen.json ADDED
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mlip_arena/tasks/combustion/mace-mp/hydrogen.json ADDED
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mlip_arena/tasks/run.py ADDED
@@ -0,0 +1,299 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ from __future__ import annotations
2
+
3
+ from datetime import datetime, timedelta
4
+ from pathlib import Path
5
+ from typing import Literal, Sequence, Tuple
6
+
7
+ import numpy as np
8
+ from ase import Atoms, units
9
+ from ase.calculators.calculator import Calculator
10
+ from ase.calculators.mixing import SumCalculator
11
+ from ase.io import read
12
+ from ase.io.trajectory import Trajectory
13
+ from ase.md.andersen import Andersen
14
+ from ase.md.langevin import Langevin
15
+ from ase.md.md import MolecularDynamics
16
+ from ase.md.npt import NPT
17
+ from ase.md.nptberendsen import NPTBerendsen
18
+ from ase.md.nvtberendsen import NVTBerendsen
19
+ from ase.md.velocitydistribution import (
20
+ MaxwellBoltzmannDistribution,
21
+ Stationary,
22
+ ZeroRotation,
23
+ )
24
+ from ase.md.verlet import VelocityVerlet
25
+ from prefect import task
26
+ from prefect.tasks import task_input_hash
27
+ from scipy.interpolate import interp1d
28
+ from scipy.linalg import schur
29
+ from torch_dftd.torch_dftd3_calculator import TorchDFTD3Calculator
30
+ from tqdm.auto import tqdm
31
+
32
+ from mlip_arena.models.utils import MLIPEnum, get_freer_device
33
+
34
+ # from mlip_arena.models.utils import EXTMLIPEnum, MLIPMap, external_ase_calculator
35
+
36
+ _valid_dynamics: dict[str, tuple[str, ...]] = {
37
+ "nve": ("velocityverlet",),
38
+ "nvt": ("nose-hoover", "langevin", "andersen", "berendsen"),
39
+ "npt": ("nose-hoover", "berendsen"),
40
+ }
41
+
42
+ _preset_dynamics: dict = {
43
+ "nve_velocityverlet": VelocityVerlet,
44
+ "nvt_andersen": Andersen,
45
+ "nvt_berendsen": NVTBerendsen,
46
+ "nvt_langevin": Langevin,
47
+ "nvt_nose-hoover": NPT,
48
+ "npt_berendsen": NPTBerendsen,
49
+ "npt_nose-hoover": NPT,
50
+ }
51
+
52
+
53
+ def _interpolate_quantity(values: Sequence | np.ndarray, n_pts: int) -> np.ndarray:
54
+ """Interpolate temperature / pressure on a schedule."""
55
+ n_vals = len(values)
56
+ return np.interp(
57
+ np.linspace(0, n_vals - 1, n_pts + 1),
58
+ np.linspace(0, n_vals - 1, n_vals),
59
+ values,
60
+ )
61
+
62
+
63
+ def _get_ensemble_schedule(
64
+ ensemble: Literal["nve", "nvt", "npt"] = "nvt",
65
+ n_steps: int = 1000,
66
+ temperature: float | Sequence | np.ndarray | None = 300.0,
67
+ pressure: float | Sequence | np.ndarray | None = None,
68
+ ) -> Tuple[np.ndarray, np.ndarray]:
69
+ if ensemble == "nve":
70
+ # Disable thermostat and barostat
71
+ temperature = np.nan
72
+ pressure = np.nan
73
+ t_schedule = np.full(n_steps + 1, temperature)
74
+ p_schedule = np.full(n_steps + 1, pressure)
75
+ return t_schedule, p_schedule
76
+
77
+ if isinstance(temperature, Sequence) or (
78
+ isinstance(temperature, np.ndarray) and temperature.ndim == 1
79
+ ):
80
+ t_schedule = _interpolate_quantity(temperature, n_steps)
81
+ # NOTE: In ASE Langevin dynamics, the temperature are normally
82
+ # scalars, but in principle one quantity per atom could be specified by giving
83
+ # an array. This is not implemented yet here.
84
+ else:
85
+ t_schedule = np.full(n_steps + 1, temperature)
86
+
87
+ if ensemble == "nvt":
88
+ pressure = np.nan
89
+ p_schedule = np.full(n_steps + 1, pressure)
90
+ return t_schedule, p_schedule
91
+
92
+ if isinstance(pressure, Sequence) or (
93
+ isinstance(pressure, np.ndarray) and pressure.ndim == 1
94
+ ):
95
+ p_schedule = _interpolate_quantity(pressure, n_steps)
96
+ elif isinstance(pressure, np.ndarray) and pressure.ndim == 4:
97
+ p_schedule = interp1d(np.arange(n_steps + 1), pressure, kind="linear")
98
+ assert isinstance(p_schedule, np.ndarray)
99
+ else:
100
+ p_schedule = np.full(n_steps + 1, pressure)
101
+
102
+ return t_schedule, p_schedule
103
+
104
+
105
+ def _get_ensemble_defaults(
106
+ ensemble: Literal["nve", "nvt", "npt"],
107
+ dynamics: str | MolecularDynamics,
108
+ t_schedule: np.ndarray,
109
+ p_schedule: np.ndarray,
110
+ ase_md_kwargs: dict | None = None,
111
+ ) -> dict:
112
+ """Update ASE MD kwargs"""
113
+ ase_md_kwargs = ase_md_kwargs or {}
114
+
115
+ if ensemble == "nve":
116
+ ase_md_kwargs.pop("temperature", None)
117
+ ase_md_kwargs.pop("temperature_K", None)
118
+ ase_md_kwargs.pop("externalstress", None)
119
+ elif ensemble == "nvt":
120
+ ase_md_kwargs["temperature_K"] = t_schedule[0]
121
+ ase_md_kwargs.pop("externalstress", None)
122
+ elif ensemble == "npt":
123
+ ase_md_kwargs["temperature_K"] = t_schedule[0]
124
+ ase_md_kwargs["externalstress"] = p_schedule[0] # * 1e3 * units.bar
125
+
126
+ if isinstance(dynamics, str) and dynamics.lower() == "langevin":
127
+ ase_md_kwargs["friction"] = ase_md_kwargs.get(
128
+ "friction",
129
+ 10.0 * 1e-3 / units.fs, # Same default as in VASP: 10 ps^-1
130
+ )
131
+
132
+ return ase_md_kwargs
133
+
134
+
135
+ @task(cache_key_fn=task_input_hash, cache_expiration=timedelta(days=1))
136
+ def md(
137
+ atoms: Atoms,
138
+ calculator_name: str | MLIPEnum,
139
+ calculator_kwargs: dict | None,
140
+ dispersion: str | None = None,
141
+ dispersion_kwargs: dict | None = None,
142
+ device: str | None = None,
143
+ ensemble: Literal["nve", "nvt", "npt"] = "nvt",
144
+ dynamics: str | MolecularDynamics = "langevin",
145
+ time_step: float | None = None,
146
+ total_time: float = 1000,
147
+ temperature: float | Sequence | np.ndarray | None = 300.0,
148
+ pressure: float | Sequence | np.ndarray | None = None,
149
+ ase_md_kwargs: dict | None = None,
150
+ mb_velocity_seed: int | None = None,
151
+ zero_linear_momentum: bool = True,
152
+ zero_angular_momentum: bool = True,
153
+ traj_file: str | Path | None = None,
154
+ traj_interval: int = 1,
155
+ restart: bool = True,
156
+ ):
157
+ device = device or str(get_freer_device())
158
+
159
+ print(f"Using device: {device}")
160
+
161
+ calculator_kwargs = calculator_kwargs or {}
162
+
163
+ if isinstance(calculator_name, MLIPEnum) and calculator_name in MLIPEnum:
164
+ assert issubclass(calculator_name.value, Calculator)
165
+ calc = calculator_name.value(**calculator_kwargs)
166
+ elif (
167
+ isinstance(calculator_name, str) and calculator_name in MLIPEnum._member_names_
168
+ ):
169
+ calc = MLIPEnum[calculator_name].value(**calculator_kwargs)
170
+ else:
171
+ raise ValueError(f"Invalid calculator: {calculator_name}")
172
+
173
+ print(f"Using calculator: {calc}")
174
+
175
+ dispersion_kwargs = dispersion_kwargs or {}
176
+
177
+ dispersion_kwargs.update({"device": device})
178
+
179
+ if dispersion is not None:
180
+ disp_calc = TorchDFTD3Calculator(
181
+ **dispersion_kwargs,
182
+ )
183
+ calc = SumCalculator([calc, disp_calc])
184
+
185
+ print(f"Using dispersion: {dispersion}")
186
+
187
+ atoms.calc = calc
188
+
189
+ if time_step is None:
190
+ # If a structure contains an isotope of hydrogen, set default `time_step`
191
+ # to 0.5 fs, and 2 fs otherwise.
192
+ has_h_isotope = "H" in atoms.get_chemical_symbols()
193
+ time_step = 0.5 if has_h_isotope else 2.0
194
+
195
+ n_steps = int(total_time / time_step)
196
+ target_steps = n_steps
197
+
198
+ t_schedule, p_schedule = _get_ensemble_schedule(
199
+ ensemble=ensemble,
200
+ n_steps=n_steps,
201
+ temperature=temperature,
202
+ pressure=pressure,
203
+ )
204
+
205
+ ase_md_kwargs = _get_ensemble_defaults(
206
+ ensemble=ensemble,
207
+ dynamics=dynamics,
208
+ t_schedule=t_schedule,
209
+ p_schedule=p_schedule,
210
+ ase_md_kwargs=ase_md_kwargs,
211
+ )
212
+
213
+ if isinstance(dynamics, str):
214
+ # Use known dynamics if `self.dynamics` is a str
215
+ dynamics = dynamics.lower()
216
+ if dynamics not in _valid_dynamics[ensemble]:
217
+ raise ValueError(
218
+ f"{dynamics} thermostat not available for {ensemble}."
219
+ f"Available {ensemble} thermostats are:"
220
+ " ".join(_valid_dynamics[ensemble])
221
+ )
222
+ if ensemble == "nve":
223
+ dynamics = "velocityverlet"
224
+ md_class = _preset_dynamics[f"{ensemble}_{dynamics}"]
225
+ elif dynamics is MolecularDynamics:
226
+ md_class = dynamics
227
+ else:
228
+ raise ValueError(f"Invalid dynamics: {dynamics}")
229
+
230
+ if md_class is NPT:
231
+ # Note that until md_func is instantiated, isinstance(md_func,NPT) is False
232
+ # ASE NPT implementation requires upper triangular cell
233
+ u, _ = schur(atoms.get_cell(complete=True), output="complex")
234
+ atoms.set_cell(u.real, scale_atoms=True)
235
+
236
+ last_step = 0
237
+
238
+ if traj_file is not None:
239
+ traj_file = Path(traj_file)
240
+ traj_file.parent.mkdir(parents=True, exist_ok=True)
241
+
242
+ if restart and traj_file.exists():
243
+ traj = read(traj_file, index=":")
244
+ last_atoms = traj[-1]
245
+ assert isinstance(last_atoms, Atoms)
246
+ last_step = last_atoms.info.get("step", len(traj) * traj_interval)
247
+ n_steps -= last_step
248
+ traj = Trajectory(traj_file, "a", atoms)
249
+ atoms.set_positions(last_atoms.get_positions())
250
+ atoms.set_momenta(last_atoms.get_momenta())
251
+ else:
252
+ traj = Trajectory(traj_file, "w", atoms)
253
+
254
+ if not np.isnan(t_schedule).any():
255
+ MaxwellBoltzmannDistribution(
256
+ atoms=atoms,
257
+ temperature_K=t_schedule[last_step],
258
+ rng=np.random.default_rng(seed=mb_velocity_seed),
259
+ )
260
+
261
+ if zero_linear_momentum:
262
+ Stationary(atoms)
263
+ if zero_angular_momentum:
264
+ ZeroRotation(atoms)
265
+
266
+ md_runner = md_class(
267
+ atoms=atoms,
268
+ timestep=time_step * units.fs,
269
+ **ase_md_kwargs,
270
+ )
271
+
272
+ if traj_file is not None:
273
+ md_runner.attach(traj.write, interval=traj_interval)
274
+
275
+ with tqdm(total=n_steps) as pbar:
276
+
277
+ def _callback(dyn: MolecularDynamics = md_runner) -> None:
278
+ step = last_step + dyn.nsteps
279
+ dyn.atoms.info["restart"] = last_step
280
+ dyn.atoms.info["datetime"] = datetime.now()
281
+ dyn.atoms.info["step"] = step
282
+ dyn.atoms.info["target_steps"] = target_steps
283
+ if ensemble == "nve":
284
+ return
285
+ dyn.set_temperature(temperature_K=t_schedule[step])
286
+ if ensemble == "nvt":
287
+ return
288
+ dyn.set_stress(p_schedule[step] * 1e3 * units.bar)
289
+ pbar.update()
290
+
291
+ md_runner.attach(_callback, interval=1)
292
+
293
+ start_time = datetime.now()
294
+ md_runner.run(steps=n_steps)
295
+ end_time = datetime.now()
296
+
297
+ traj.close()
298
+
299
+ return {"runtime": end_time - start_time, "n_steps": n_steps}
serve/app.py CHANGED
@@ -30,25 +30,25 @@ search = st.Page("tools/search.py", title="Search", icon=":material/search:")
30
  history = st.Page("tools/history.py", title="History", icon=":material/history:")
31
  ptable = st.Page("tools/ptable.py", title="Periodic table", icon=":material/gradient:")
32
 
33
- diatomics = st.Page("tasks/homonuclear-diatomics.py", title="Homonuclear Diatomics", icon=":material/target:", default=True)
34
- stability = st.Page("tasks/stability.py", title="High Pressure Stability", icon=":material/target:")
 
35
 
36
 
37
  # if st.session_state.logged_in:
38
  pg = st.navigation(
39
  {
40
  # "Account": [logout_page],
41
- # "Reports": [dashboard, bugs, alerts],
42
- # "Tools": [search, history, ptable],
43
  "": [leaderboard],
44
- "Tasks": [diatomics, stability],
 
45
  "Tools": [ptable],
46
  }
47
  )
48
  # else:
49
  # pg = st.navigation([login_page])
50
 
51
- if pg in [stability]:
52
  st.set_page_config(
53
  layout="centered",
54
  page_title="MLIP Arena",
 
30
  history = st.Page("tools/history.py", title="History", icon=":material/history:")
31
  ptable = st.Page("tools/ptable.py", title="Periodic table", icon=":material/gradient:")
32
 
33
+ diatomics = st.Page("tasks/homonuclear-diatomics.py", title="Homonuclear diatomics", icon=":material/target:", default=True)
34
+ stability = st.Page("tasks/stability.py", title="High pressure stability", icon=":material/target:")
35
+ combustion = st.Page("tasks/combustion.py", title="Combustion", icon=":material/target:")
36
 
37
 
38
  # if st.session_state.logged_in:
39
  pg = st.navigation(
40
  {
41
  # "Account": [logout_page],
 
 
42
  "": [leaderboard],
43
+ "Fundamentals": [diatomics],
44
+ "Molecular Dynamics": [stability, combustion],
45
  "Tools": [ptable],
46
  }
47
  )
48
  # else:
49
  # pg = st.navigation([login_page])
50
 
51
+ if pg in [stability, combustion]:
52
  st.set_page_config(
53
  layout="centered",
54
  page_title="MLIP Arena",
serve/models/leaderboard.py CHANGED
@@ -3,7 +3,8 @@ from pathlib import Path
3
  import pandas as pd
4
  import streamlit as st
5
 
6
- from mlip_arena.models.utils import MLIPEnum, REGISTRY
 
7
 
8
  DATA_DIR = Path("mlip_arena/tasks/diatomics")
9
  methods = ["MACE-MP", "Equiformer", "CHGNet", "MACE-OFF", "eSCN", "ALIGNN"]
@@ -19,16 +20,16 @@ table = pd.DataFrame(columns=[
19
  "Prediction",
20
  "NVT",
21
  "NPT",
22
- "Last updated",
23
  "Code",
24
- "Paper"
 
25
  ])
26
 
27
- for model in MLIPEnum:
28
- rows = df[df["method"] == model.name]
29
- metadata = REGISTRY.get(model.name, {})
30
  new_row = {
31
- "Model": model.name,
32
  "Supported elements": len(rows["name"].unique()),
33
  # "No. of reversed forces": None, # Replace with actual logic if available
34
  # "Energy-consistent forces": None, # Replace with actual logic if available
 
3
  import pandas as pd
4
  import streamlit as st
5
 
6
+ # from mlip_arena.models.utils import MLIPEnum, REGISTRY
7
+ from mlip_arena.models import REGISTRY
8
 
9
  DATA_DIR = Path("mlip_arena/tasks/diatomics")
10
  methods = ["MACE-MP", "Equiformer", "CHGNet", "MACE-OFF", "eSCN", "ALIGNN"]
 
20
  "Prediction",
21
  "NVT",
22
  "NPT",
 
23
  "Code",
24
+ "Paper",
25
+ "Last updated",
26
  ])
27
 
28
+ for model in REGISTRY:
29
+ rows = df[df["method"] == model]
30
+ metadata = REGISTRY.get(model, {})
31
  new_row = {
32
+ "Model": model,
33
  "Supported elements": len(rows["name"].unique()),
34
  # "No. of reversed forces": None, # Replace with actual logic if available
35
  # "Energy-consistent forces": None, # Replace with actual logic if available
serve/tasks/combustion.py ADDED
@@ -0,0 +1,132 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ from pathlib import Path
2
+
3
+ import numpy as np
4
+ import pandas as pd
5
+ import plotly.colors as pcolors
6
+ import plotly.express as px
7
+ import plotly.graph_objects as go
8
+ import streamlit as st
9
+ from scipy.optimize import curve_fit
10
+
11
+ from mlip_arena.models import REGISTRY
12
+
13
+ DATA_DIR = Path("mlip_arena/tasks/combustion")
14
+
15
+
16
+ st.markdown("""
17
+ # Combustion
18
+ """)
19
+
20
+ st.markdown("### Methods")
21
+ container = st.container(border=True)
22
+ valid_models = [model for model, metadata in REGISTRY.items() if Path(__file__).stem in metadata.get("gpu-tasks", [])]
23
+
24
+ models = container.multiselect("MLIPs", valid_models, ["MACE-MP(M)", "CHGNet", "EquiformerV2(OC22)"])
25
+
26
+ st.markdown("### Settings")
27
+ vis = st.container(border=True)
28
+ # Get all attributes from pcolors.qualitative
29
+ all_attributes = dir(pcolors.qualitative)
30
+ color_palettes = {
31
+ attr: getattr(pcolors.qualitative, attr)
32
+ for attr in all_attributes
33
+ if isinstance(getattr(pcolors.qualitative, attr), list)
34
+ }
35
+ color_palettes.pop("__all__", None)
36
+
37
+ palette_names = list(color_palettes.keys())
38
+ palette_colors = list(color_palettes.values())
39
+
40
+ palette_name = vis.selectbox("Color sequence", options=palette_names, index=22)
41
+
42
+ color_sequence = color_palettes[palette_name]
43
+
44
+ if not models:
45
+ st.stop()
46
+
47
+ families = [REGISTRY[str(model)]["family"] for model in models]
48
+
49
+ dfs = [
50
+ pd.read_json(DATA_DIR / family.lower() / "hydrogen.json")
51
+ for family in families
52
+ ]
53
+ df = pd.concat(dfs, ignore_index=True)
54
+ df.drop_duplicates(inplace=True, subset=["formula", "method"])
55
+
56
+ method_color_mapping = {
57
+ method: color_sequence[i % len(color_sequence)]
58
+ for i, method in enumerate(df["method"].unique())
59
+ }
60
+
61
+ ###
62
+
63
+ # Number of products
64
+ fig = go.Figure()
65
+
66
+ for method in df["method"].unique():
67
+ row = df[df["method"] == method].iloc[0]
68
+ fig.add_trace(
69
+ go.Scatter(
70
+ x=row["timesteps"],
71
+ y=row["nproducts"],
72
+ mode='lines',
73
+ name=method,
74
+ line=dict(color=method_color_mapping[method]),
75
+ showlegend=True,
76
+ ),
77
+ )
78
+
79
+ fig.update_layout(
80
+ title="Hydrogen Combusiton (2H2 + O2 -> 2H2O, 64 units)",
81
+ xaxis_title="Timesteps",
82
+ yaxis_title="Number of water molecules",
83
+ )
84
+
85
+ st.plotly_chart(fig)
86
+
87
+ # tempearture
88
+
89
+ fig = go.Figure()
90
+
91
+ for method in df["method"].unique():
92
+ row = df[df["method"] == method].iloc[0]
93
+ fig.add_trace(
94
+ go.Scatter(
95
+ x=row["timesteps"],
96
+ y=row["temperatures"],
97
+ mode='markers',
98
+ name=method,
99
+ line=dict(color=method_color_mapping[method]),
100
+ showlegend=True,
101
+ ),
102
+ )
103
+
104
+ target_steps = df["target_steps"].iloc[0]
105
+ fig.add_trace(
106
+ go.Line(
107
+ x=[0, target_steps/3, target_steps/3*2, target_steps],
108
+ y=[300, 3000, 3000, 300],
109
+ mode='lines',
110
+ name="Target",
111
+ line=dict(
112
+ dash="dash",
113
+ ),
114
+ showlegend=True,
115
+ ),
116
+ )
117
+
118
+ fig.update_layout(
119
+ title="Hydrogen Combusiton (2H2 + O2 -> 2H2O, 64 units)",
120
+ xaxis_title="Timesteps",
121
+ yaxis_title="Temperatures",
122
+ yaxis2=dict(
123
+ title="Product Percentage (%)",
124
+ overlaying="y",
125
+ side="right",
126
+ range=[0, 100],
127
+ tickmode="sync"
128
+ )
129
+ # template="plotly_dark",
130
+ )
131
+
132
+ st.plotly_chart(fig)