Adsorption Energies in MOFs

Pre-trained ODAC models are versatile across various MOF-related tasks. To begin, we’ll start with a fundamental application: calculating the adsorption energy for a single CO₂ molecule. This serves as an excellent and simple demonstration of what you can achieve with these datasets and models.

For predicting the adsorption energy of a single CO₂ molecule within a MOF structure, the adsorption energy ( $E_{\mathrm{ads}}$ ) is defined as:

E_{\mathrm{ads}} = E_{\mathrm{MOF+CO2}} - E_{\mathrm{MOF}} - E_{\mathrm{CO2}} \tag{1}

(1)

Each term on the right-hand side represents the energy of the relaxed state of the indicated chemical system. For a comprehensive understanding of our methodology for computing these adsorption energies, please refer to our paper.

Loading Pre-trained Models¶

Need to install fairchem-core or get UMA access or getting permissions/401 errors?

Install the necessary packages using pip, uv etc

! pip install fairchem-core fairchem-data-oc fairchem-applications-cattsunami

Get access to any necessary huggingface gated models
- Get and login to your Huggingface account
- Request access to https://huggingface.co/facebook/UMA
- Create a Huggingface token at https://huggingface.co/settings/tokens/ with the permission “Permissions: Read access to contents of all public gated repos you can access”
- Add the token as an environment variable using huggingface-cli login or by setting the HF_TOKEN environment variable.

# Login using the huggingface-cli utility
! huggingface-cli login

# alternatively,
import os
os.environ['HF_TOKEN'] = 'MY_TOKEN'

A pre-trained model can be loaded using FAIRChemCalculator. In this example, we’ll employ UMA to determine the CO₂ adsorption energies.

from fairchem.core import FAIRChemCalculator, pretrained_mlip

predictor = pretrained_mlip.get_predict_unit("uma-s-1p1")
calc = FAIRChemCalculator(predictor, task_name="odac")

WARNING:root:device was not explicitly set, using device='cuda'.

Adsorption in rigid MOFs: CO₂ Adsorption Energy in Mg-MOF-74¶

Let’s apply our knowledge to Mg-MOF-74, a widely studied MOF known for its excellent CO₂ adsorption properties. Its structure comprises magnesium atomic complexes connected by a carboxylated and oxidized benzene ring, serving as an organic linker. Previous studies consistently report the CO₂ adsorption energy for Mg-MOF-74 to be around -0.40 eV [1] [2] [3].

Our goal is to verify if we can achieve a similar value by performing a simple single-point calculation using UMA. In the ODAC23 dataset, all MOF structures are identified by their CSD (Cambridge Structural Database) code. For Mg-MOF-74, this code is OPAGIX. We’ve extracted a specific OPAGIX+CO2 configuration from the dataset, which exhibits the lowest adsorption energy among its counterparts.

import matplotlib.pyplot as plt
from ase.io import read
from ase.visualize.plot import plot_atoms

mof_co2 = read("structures/OPAGIX_w_CO2.cif")
mof = read("structures/OPAGIX.cif")
co2 = read("structures/co2.xyz")

fig, ax = plt.subplots(figsize=(5, 4.5), dpi=250)
plot_atoms(mof_co2, ax)
ax.set_axis_off()

The final step in calculating the adsorption energy involves connecting the FAIRChemCalculator to each relaxed structure: OPAGIX+CO2, OPAGIX, and CO2. The structures used here are already relaxed from ODAC23. For simplicity, we assume here that further relaxations can be neglected. We will show how to go beyond this assumption in the next section.

mof_co2.calc = calc
mof.calc = calc
co2.calc = calc

E_ads = (
    mof_co2.get_potential_energy()
    - mof.get_potential_energy()
    - co2.get_potential_energy()
)

print(f"Adsorption energy of CO2 in Mg-MOF-74: {E_ads:.3f} eV")

Adsorption energy of CO2 in Mg-MOF-74: -0.459 eV

Adsorption in flexible MOFs¶

The adsorption energy calculation method outlined above is typically performed with rigid MOFs for simplicity. Both experimental and modeling literature have shown, however, that MOF flexibility can be important in accurately capturing the underlying chemistry of adsorption [1] [2] [3]. In particular, uptake can be improved by treating MOFs as flexible. Two types of MOF flexibility can be considered: intrinsic flexibility and deformation induced by guest molecules. In the Open DAC Project, we consider the latter MOF deformation by allowing the atomic positions of the MOF to relax during geometry optimization [4]. The addition of additional degrees of freedoms can complicate the computation of the adsorption energy and necessitates an extra step in the calculation procedure.

The figure below shows water adsorption in the MOF with CSD code WOBHEB with added defects (WOBHEB_0.11_0) from a DFT simulation. A typical adsorption energy calculation would only seek to capture the effects shaded in purple, which include both chemisorption and non-bonded interactions between the host and guest molecule. When allowing the MOF to relax, however, the adsorption energy also includes the energetic effect of the MOF deformation highlighted in green.

To account for this deformation, it is vital to use the most energetically favorable MOF geometry for the empty MOF term in Eqn. 1. Including MOF atomic coordinates as degrees of freedom can result in three possible outcomes:

The MOF does not deform, so the energies of the relaxed empty MOF and the MOF in the adsorbed state are the same
The MOF deforms to a less energetically favorable geometry than its ground state
The MOF locates a new energetically favorable geoemtry relative to the empty MOF relaxation

The first outcome requires no additional computation because the MOF rigidity assumption is valid. The second outcome represents physical and reversible deformation where the MOF returns to its empty ground state upon removal of the guest molecule. The third outcome is often the result of the guest molecule breaking local symmetry. We also found cases in ODAC in which both outcomes 2 and 3 occur within the same MOF.

To ensure the most energetically favorable empty MOF geometry is found, an addition empty MOF relaxation should be performed after MOF + adsorbate relaxation. The guest molecule should be removed, and the MOF should be relaxed starting from its geometry in the adsorbed state. If all deformation is reversible, the MOF will return to its original empty geometry. Otherwise, the lowest energy (most favorable) MOF geometry should be taken as the reference energy, $E_{\mathrm{MOF}}$ , in Eqn. 1.

H₂O Adsorption Energy in Flexible WOBHEB with UMA¶

The first part of this tutorial demonstrates how to perform a single point adsorption energy calculation using UMA. To treat MOFs as flexible, we perform all calculations on geometries determined by geometry optimization. The following example corresponds to the figure shown above (H₂O adsorption in WOBHEB_0.11_0).

In this tutorial, $E_{x}(r_{y})$ corresponds to the energy of $x$ determined from geometry optimization of $y$ .

First, we obtain the energy of the empty MOF from relaxation of only the MOF: $E_{\mathrm{MOF}}(r_{\mathrm{MOF}})$

import ase.io
from ase.optimize import BFGS

mof = ase.io.read("structures/WOBHEB_0.11.cif")
mof.calc = calc
relax = BFGS(mof)
relax.run(fmax=0.05)
E_mof_empty = mof.get_potential_energy()
print(f"Energy of empty MOF: {E_mof_empty:.3f} eV")

      Step     Time          Energy          fmax
BFGS:    0 19:24:56    -1077.274062        0.206406

BFGS:    1 19:24:57    -1077.276780        0.152729

BFGS:    2 19:24:58    -1077.281941        0.169932

BFGS:    3 19:25:00    -1077.284772        0.155752

BFGS:    4 19:25:00    -1077.288839        0.108767

BFGS:    5 19:25:01    -1077.291021        0.086433

BFGS:    6 19:25:02    -1077.293361        0.093433

BFGS:    7 19:25:03    -1077.295409        0.100141

BFGS:    8 19:25:04    -1077.297831        0.102538

BFGS:    9 19:25:04    -1077.300015        0.091599

BFGS:   10 19:25:05    -1077.302008        0.079035

BFGS:   11 19:25:05    -1077.304139        0.105595

BFGS:   12 19:25:06    -1077.306720        0.087897

BFGS:   13 19:25:07    -1077.309520        0.086343

BFGS:   14 19:25:08    -1077.312260        0.086824

BFGS:   15 19:25:09    -1077.314703        0.106300

BFGS:   16 19:25:10    -1077.316991        0.106201

BFGS:   17 19:25:12    -1077.319485        0.085516

BFGS:   18 19:25:13    -1077.322264        0.109632

BFGS:   19 19:25:13    -1077.325134        0.148691

BFGS:   20 19:25:16    -1077.327765        0.125945

BFGS:   21 19:25:17    -1077.329920        0.069098

BFGS:   22 19:25:17    -1077.331955        0.087284

BFGS:   23 19:25:18    -1077.334273        0.125221

BFGS:   24 19:25:18    -1077.336833        0.166687

BFGS:   25 19:25:20    -1077.339544        0.145559

BFGS:   26 19:25:20    -1077.342149        0.087648

BFGS:   27 19:25:21    -1077.344541        0.076175

BFGS:   28 19:25:24    -1077.346890        0.148943

BFGS:   29 19:25:26    -1077.349783        0.170213

BFGS:   30 19:25:29    -1077.352529        0.109285

BFGS:   31 19:25:31    -1077.354746        0.070348

BFGS:   32 19:25:32    -1077.356776        0.089669

BFGS:   33 19:25:33    -1077.358658        0.124289

BFGS:   34 19:25:34    -1077.360604        0.108035

BFGS:   35 19:25:35    -1077.362469        0.068621

BFGS:   36 19:25:35    -1077.364167        0.070223

BFGS:   37 19:25:37    -1077.365715        0.105371

BFGS:   38 19:25:37    -1077.367258        0.104407

BFGS:   39 19:25:38    -1077.368765        0.062612

BFGS:   40 19:25:39    -1077.370120        0.057926

BFGS:   41 19:25:40    -1077.371336        0.064509

BFGS:   42 19:25:41    -1077.372380        0.067290

BFGS:   43 19:25:41    -1077.373369        0.057369

BFGS:   44 19:25:42    -1077.374404        0.042166
Energy of empty MOF: -1077.374 eV

Next, we add the H₂O guest molecule and relax the MOF + adsorbate to obtain $E_{\mathrm{MOF+H2O}}(r_{\mathrm{MOF+H2O}})$ .

mof_h2o = ase.io.read("structures/WOBHEB_H2O.cif")
mof_h2o.calc = calc
relax = BFGS(mof_h2o)
relax.run(fmax=0.05)
E_combo = mof_h2o.get_potential_energy()
print(f"Energy of MOF + H2O: {E_combo:.3f} eV")

      Step     Time          Energy          fmax
BFGS:    0 19:25:43    -1091.565588        1.145036

BFGS:    1 19:25:46    -1091.585063        0.314149

BFGS:    2 19:25:47    -1091.590210        0.243429

BFGS:    3 19:25:48    -1091.608170        0.237244

BFGS:    4 19:25:49    -1091.614633        0.227934

BFGS:    5 19:25:50    -1091.625219        0.186790

BFGS:    6 19:25:51    -1091.632357        0.178916

BFGS:    7 19:25:52    -1091.640624        0.175081

BFGS:    8 19:25:53    -1091.648042        0.184522

BFGS:    9 19:25:54    -1091.656144        0.160935

BFGS:   10 19:25:55    -1091.663843        0.178420

BFGS:   11 19:25:57    -1091.672299        0.188697

BFGS:   12 19:25:58    -1091.682088        0.157552

BFGS:   13 19:25:59    -1091.692985        0.177272

BFGS:   14 19:26:00    -1091.704431        0.158116

BFGS:   15 19:26:01    -1091.715508        0.191678

BFGS:   16 19:26:02    -1091.725712        0.197897

BFGS:   17 19:26:04    -1091.735324        0.163753

BFGS:   18 19:26:05    -1091.745537        0.151472

BFGS:   19 19:26:06    -1091.754018        0.170835

BFGS:   20 19:26:08    -1091.761494        0.153898

BFGS:   21 19:26:08    -1091.767888        0.152411

BFGS:   22 19:26:09    -1091.774205        0.165885

BFGS:   23 19:26:10    -1091.780891        0.135476

BFGS:   24 19:26:11    -1091.788350        0.181065

BFGS:   25 19:26:12    -1091.794277        0.204681

BFGS:   26 19:26:12    -1091.800635        0.131379

BFGS:   27 19:26:13    -1091.806516        0.189491

BFGS:   28 19:26:14    -1091.812299        0.199155

BFGS:   29 19:26:15    -1091.817182        0.151652

BFGS:   30 19:26:16    -1091.822210        0.100141

BFGS:   31 19:26:16    -1091.826298        0.125828

BFGS:   32 19:26:17    -1091.832526        0.177421

BFGS:   33 19:26:19    -1091.837110        0.246726

BFGS:   34 19:26:20    -1091.842045        0.112496

BFGS:   35 19:26:21    -1091.845782        0.328688

BFGS:   36 19:26:23    -1091.850879        0.174085

BFGS:   37 19:26:24    -1091.858481        0.159914

BFGS:   38 19:26:24    -1091.865287        0.141291

BFGS:   39 19:26:25    -1091.872005        0.141917

BFGS:   40 19:26:25    -1091.878338        0.247148

BFGS:   41 19:26:27    -1091.880920        0.544926

BFGS:   42 19:26:28    -1091.885629        0.678812

BFGS:   43 19:26:28    -1091.893771        0.216384

BFGS:   44 19:26:29    -1091.899163        0.158099

BFGS:   45 19:26:30    -1091.917105        0.272864

BFGS:   46 19:26:31    -1091.924893        0.362183

BFGS:   47 19:26:33    -1091.941181        0.259165

BFGS:   48 19:26:34    -1091.956498        0.432599

BFGS:   49 19:26:34    -1091.973704        0.775537

BFGS:   50 19:26:36    -1091.966208        1.497817

BFGS:   51 19:26:36    -1092.007345        0.396691

BFGS:   52 19:26:37    -1092.022490        0.285749

BFGS:   53 19:26:38    -1092.072403        0.333420

BFGS:   54 19:26:38    -1092.088434        0.294865

BFGS:   55 19:26:39    -1092.122184        0.319618

BFGS:   56 19:26:40    -1092.127127        0.599146

BFGS:   57 19:26:41    -1092.142228        0.318145

BFGS:   58 19:26:42    -1092.157725        0.268180

BFGS:   59 19:26:43    -1092.169612        0.281630

BFGS:   60 19:26:44    -1092.182070        0.247304

BFGS:   61 19:26:45    -1092.191658        0.349909

BFGS:   62 19:26:45    -1092.203893        0.413967

BFGS:   63 19:26:46    -1092.214866        0.360631

BFGS:   64 19:26:48    -1092.224796        0.242091

BFGS:   65 19:26:51    -1092.232279        0.120391

BFGS:   66 19:26:53    -1092.238301        0.121883

BFGS:   67 19:26:54    -1092.243741        0.127485

BFGS:   68 19:26:55    -1092.249530        0.138036

BFGS:   69 19:26:56    -1092.254531        0.133713

BFGS:   70 19:26:56    -1092.259746        0.114452

BFGS:   71 19:26:57    -1092.264405        0.080882

BFGS:   72 19:26:58    -1092.268434        0.107020

BFGS:   73 19:27:01    -1092.272104        0.135069

BFGS:   74 19:27:02    -1092.275584        0.189900

BFGS:   75 19:27:02    -1092.278848        0.195108

BFGS:   76 19:27:03    -1092.282338        0.145852

BFGS:   77 19:27:04    -1092.285210        0.099664

BFGS:   78 19:27:05    -1092.287629        0.101675

BFGS:   79 19:27:07    -1092.290099        0.113556

BFGS:   80 19:27:07    -1092.292056        0.106041

BFGS:   81 19:27:08    -1092.293817        0.080004

BFGS:   82 19:27:10    -1092.295233        0.088615

BFGS:   83 19:27:11    -1092.296598        0.081745

BFGS:   84 19:27:12    -1092.298022        0.078294

BFGS:   85 19:27:13    -1092.299568        0.077982

BFGS:   86 19:27:14    -1092.301357        0.092660

BFGS:   87 19:27:16    -1092.302951        0.099303

BFGS:   88 19:27:16    -1092.304805        0.093478

BFGS:   89 19:27:17    -1092.306296        0.067367

BFGS:   90 19:27:18    -1092.307679        0.048825
Energy of MOF + H2O: -1092.308 eV

We can now isolate the MOF atoms from the relaxed MOF + H₂O geometry and see that the MOF has adopted a geometry that is less energetically favorable than the empty MOF by ~0.2 eV. The energy of the MOF in the adsorbed state corresponds to $E_{\mathrm{MOF}}(r_{\mathrm{MOF+H2O}})$ .

mof_adsorbed_state = mof_h2o[:-3]
mof_adsorbed_state.calc = calc
E_mof_adsorbed_state = mof_adsorbed_state.get_potential_energy()
print(f"Energy of MOF in the adsorbed state: {E_mof_adsorbed_state:.3f} eV")

Energy of MOF in the adsorbed state: -1077.091 eV

H₂O adsorption in this MOF appears to correspond to Case #2 as outlined above. We can now perform re-relaxation of the empty MOF starting from the $r_{\mathrm{MOF+H2O}}$ geometry.

relax = BFGS(mof_adsorbed_state)
relax.run(fmax=0.05)
E_mof_rerelax = mof_adsorbed_state.get_potential_energy()
print(f"Energy of re-relaxed empty MOF: {E_mof_rerelax:.3f} eV")

      Step     Time          Energy          fmax
BFGS:    0 19:27:18    -1077.090553        0.983390

BFGS:    1 19:27:19    -1077.122796        0.871949

BFGS:    2 19:27:20    -1077.172480        0.801385

BFGS:    3 19:27:21    -1077.212450        0.502580

BFGS:    4 19:27:22    -1077.232150        0.439210

BFGS:    5 19:27:23    -1077.250061        0.282549

BFGS:    6 19:27:24    -1077.260795        0.260939

BFGS:    7 19:27:25    -1077.270315        0.246514

BFGS:    8 19:27:26    -1077.279564        0.209651

BFGS:    9 19:27:27    -1077.286616        0.183123

BFGS:   10 19:27:29    -1077.291780        0.151377

BFGS:   11 19:27:30    -1077.295884        0.140513

BFGS:   12 19:27:32    -1077.299531        0.145093

BFGS:   13 19:27:33    -1077.304188        0.184344

BFGS:   14 19:27:34    -1077.308955        0.172553

BFGS:   15 19:27:35    -1077.313551        0.136378

BFGS:   16 19:27:35    -1077.317677        0.164919

BFGS:   17 19:27:36    -1077.321891        0.150550

BFGS:   18 19:27:37    -1077.326254        0.143855

BFGS:   19 19:27:39    -1077.329819        0.113696

BFGS:   20 19:27:39    -1077.332449        0.109350

BFGS:   21 19:27:40    -1077.334689        0.099863

BFGS:   22 19:27:43    -1077.336976        0.121523

BFGS:   23 19:27:44    -1077.339681        0.117428

BFGS:   24 19:27:46    -1077.342297        0.090415

BFGS:   25 19:27:47    -1077.344617        0.090821

BFGS:   26 19:27:49    -1077.346519        0.068008

BFGS:   27 19:27:50    -1077.348068        0.067778

BFGS:   28 19:27:51    -1077.349774        0.091663

BFGS:   29 19:27:52    -1077.351328        0.095574

BFGS:   30 19:27:53    -1077.352936        0.064592

BFGS:   31 19:27:54    -1077.354117        0.050613

BFGS:   32 19:27:55    -1077.355418        0.055644

BFGS:   33 19:27:58    -1077.356790        0.084699

BFGS:   34 19:27:58    -1077.358186        0.079266

BFGS:   35 19:27:59    -1077.359702        0.068661

BFGS:   36 19:28:00    -1077.361108        0.070816

BFGS:   37 19:28:00    -1077.362385        0.074361

BFGS:   38 19:28:01    -1077.363568        0.085450

BFGS:   39 19:28:02    -1077.364887        0.080212

BFGS:   40 19:28:03    -1077.365862        0.066907

BFGS:   41 19:28:04    -1077.366878        0.058635

BFGS:   42 19:28:04    -1077.367970        0.059731

BFGS:   43 19:28:05    -1077.369004        0.069902

BFGS:   44 19:28:08    -1077.370253        0.075623

BFGS:   45 19:28:09    -1077.371406        0.048885
Energy of re-relaxed empty MOF: -1077.371 eV

The MOF returns to its original empty reference energy upon re-relaxation, confirming that this deformation is physically relevant and is induced by the adsorbate molecule. In Case #3, this re-relaxed energy will be more negative (more favorable) than the original empty MOF relaxation. Thus, we take the reference empty MOF energy ( $E_{\mathrm{MOF}}$ in Eqn. 1) to be the minimum of the original empty MOF energy and the re-relaxed MOf energy:

E_mof = min(E_mof_empty, E_mof_rerelax)

# get adsorbate reference energy
h2o = mof_h2o[-3:]
h2o.calc = calc
E_h2o = h2o.get_potential_energy()

# compute adsorption energy
E_ads = E_combo - E_mof - E_h2o
print(f"Adsorption energy of H2O in WOBHEB_0.11_0: {E_ads:.3f} eV")

Adsorption energy of H2O in WOBHEB_0.11_0: -0.685 eV

This adsorption energy closely matches that from DFT (–0.699 eV) [1]. The strong adsorption energy is a consequence of both H₂O chemisorption and MOF deformation. We can decompose the adsorption energy into contributions from these two factors. Assuming rigid H₂O molecules, we define $E_{\mathrm{int}}$ and $E_{\mathrm{MOF,deform}}$ , respectively, as

E_{\mathrm{int}} = E_{\mathrm{MOF+H2O}}(r_{\mathrm{MOF+H2O}}) - E_{\mathrm{MOF}}(r_{\mathrm{MOF+H2O}}) - E_{\mathrm{H2O}}(r_{\mathrm{MOF+H2O}}) \tag{2}

(2)

E_{\mathrm{MOF,deform}} = E_{\mathrm{MOF}}(r_{\mathrm{MOF+H2O}}) - E_{\mathrm{MOF}}(r_{\mathrm{MOF}}) \tag{3}

(3)

$E_{\mathrm{int}}$ describes host host–guest interactions for the MOF in the adsorbed state only. $E_{\mathrm{MOF,deform}}$ quantifies the magnitude of deformation between the MOF in the adsorbed state and the most energetically favorable empty MOF geometry determined from the workflow presented here. It can be shown that

E_{\mathrm{ads}} = E_{\mathrm{int}} + E_{\mathrm{MOF,deform}} \tag{4}

(4)

For H₂O adsorption in WOBHEB_0.11, we have

E_int = E_combo - E_mof_adsorbed_state - E_h2o
print(f"E_int: {E_int}")

E_int: -0.9691989421842617

E_mof_deform = E_mof_adsorbed_state - E_mof_empty
print(f"E_mof_deform: {E_mof_deform}")

E_mof_deform: 0.28385066986083984

E_ads = E_int + E_mof_deform
print(f"E_ads: {E_ads}")

E_ads: -0.6853482723234219

$E_{\mathrm{int}}$ is equivalent to $E_{\mathrm{ads}}$ when the MOF is assumed to be rigid. In this case, failure to consider adsorbate-induced deformation would result in an overestimation of the adsorption energy magnitude.

Acknowledgements & Authors¶

Logan Brabson and Sihoon Choi (Georgia Tech) and the OpenDAC project.

References¶

Sriram, A., Choi, S., Yu, X., Brabson, L. M., Das, A., Ulissi, Z., Uyttendaele, M., Medford, A. J., & Sholl, D. S. (2024). The Open DAC 2023 Dataset and Challenges for Sorbent Discovery in Direct Air Capture. ACS Central Science, 10(5), 923–941. 10.1021/acscentsci.3c01629
Queen, W. L., Hudson, M. R., Bloch, E. D., Mason, J. A., Gonzalez, M. I., Lee, J. S., Gygi, D., Howe, J. D., Lee, K., Darwish, T. A., James, M., Peterson, V. K., Teat, S. J., Smit, B., Neaton, J. B., Long, J. R., & Brown, C. M. (2014). Comprehensive study of carbon dioxide adsorption in the metal–organic frameworks M2(dobdc) (M = Mg, Mn, Fe, Co, Ni, Cu, Zn). Chem. Sci., 5(12), 4569–4581. 10.1039/c4sc02064b
Yu, D., Yazaydin, A. O., Lane, J. R., Dietzel, P. D. C., & Snurr, R. Q. (2013). A combined experimental and quantum chemical study of CO2 adsorption in the metal–organic framework CPO-27 with different metals. Chemical Science, 4(9), 3544. 10.1039/c3sc51319j
Alonso, G., Bahamon, D., Keshavarz, F., Giménez, X., Gamallo, P., & Sayós, R. (2018). Density Functional Theory-Based Adsorption Isotherms for Pure and Flue Gas Mixtures on Mg-MOF-74. Application in CO2 Capture Swing Adsorption Processes. The Journal of Physical Chemistry C, 122(7), 3945–3957. 10.1021/acs.jpcc.8b00938
Witman, M., Ling, S., Jawahery, S., Boyd, P. G., Haranczyk, M., Slater, B., & Smit, B. (2017). The Influence of Intrinsic Framework Flexibility on Adsorption in Nanoporous Materials. Journal of the American Chemical Society, 139(15), 5547–5557. 10.1021/jacs.7b01688

Loading Pre-trained Models¶

Adsorption in rigid MOFs: CO2 Adsorption Energy in Mg-MOF-74¶

Adsorption in flexible MOFs¶

H2O Adsorption Energy in Flexible WOBHEB with UMA¶

Acknowledgements & Authors¶

Adsorption in rigid MOFs: CO₂ Adsorption Energy in Mg-MOF-74¶

H₂O Adsorption Energy in Flexible WOBHEB with UMA¶