A process-level perspective of the impact of molecular force fields on the computational screening of MOFs for carbon capture

Abstract

The question we pose in this study is to what extent the ranking of metal organic frameworks (MOFs) for adsorption-based carbon capture, and the selection of top performers identified in...

A Scalable Robust Microporous Al‐MOF for Post‐Combustion Carbon Capture

“…In fact, if the performances are crucial features for a given application, other aspects including the material's cost, sustainability, chemical and thermal stability, and processability, are also important criteria to take into account, in particular when it potentially implies a very large-scale application such as CO 2 capture requiring a huge amount of adsorbents, i.e., more than a few hundred tons per plant. [34] TGA coupled with variabletemperature PXRD analysis (Figure S4, Supporting Information), confirmed that MIL-120(Al) exhibits excellent thermal stability (up to 400 °C), although some small variations in diffracted Bragg peaks could be observed starting from 100 °C, very likely due to slight structural flexibility induced by guest removal and/or some bond rearrangement (e.g., OH groups, etc.). Moreover, MIL-120(Al)-AP also showed exceptional hydrolytic stability, withstanding boiling water for (at least) 10 days as confirmed by the well-preserved crystallinity, chemical composition, morphology, thermal stability, and CO 2 capacity, as per the provided PXRD and CO 2 adsorption data, respectively (Figure S5, Supporting Information).…”

How Can (or Why Should) Process Engineering Aid the Screening and Discovery of Solid Sorbents for CO2 Capture?

“…MAPLE provided the results almost instantaneously in both the classification and minimum energy calculations, whereas detailed models took considerable computational time. The efficacy of MAPLE has been independently verified, and similar frameworks have been developed for related separations, such as direct air capture …”

“…These are difficult problems. Although predicting CO 2 /N 2 behavior on MOFs seems to be relatively easier, recent works show how the choice of force fields, even for simpler molecules such a CO 2 , is not straightforward and can have a substantial impact on predicted process performance . Further, the adsorption of H 2 O and its impact on CO 2 adsorption is poorly understood, and very little experimental and simulation data are available to validate atomistic simulations .…”

Multiscale Modeling of Physical Properties of Nanoporous Frameworks: Predicting Mechanical, Thermal, and Adsorption Behavior

“…Another approach is to rank materials in a screening study for a given application and varying the FFs, then assigning uncertainty on the outcomes related to the underlying model. 64 Since numerous screening studies rely on shared assumptions and database structures, the community would benefit from disseminating reference data derived from simulations. 65,66 This would improve computational reproducibility and facilitate the evaluation accuracy for training data sets, particularly in the context of recent machine learning models that leverage GCMC results for fast predictions of MOF properties.…”

“…To overcome these limitations, one generally proceeds in two steps: first, screening all structures of a database for targeted properties using generic FFs and clean experimental structures; second, refining the ranking after FF and geometry optimization on the best-performing materials. Another approach is to rank materials in a screening study for a given application and varying the FFs, then assigning uncertainty on the outcomes related to the underlying model …”