A Data-Science Approach to Predict the Heat Capacity of Nanoporous Materials

Abstract

The heat capacity of a material is a fundamental property that is of significant practical importance. For example, in a carbon capture process, the heat required to regenerate a solid sorbent is directly related to the heat capacity of the material. However, for most materials suitable for carbon capture applications the heat capacity is not known, and thus the standard procedure is to assume the same value for all materials. In this work, we developed a machine-learning approach to accurately predict the heat capacity of these materials, i.e., zeolites, metal-organic frameworks, and covalent-organic frameworks. The accuracy of our prediction is confirmed with novel experimental data. Finally, for a temperature swing adsorption process that captures carbon from the flue gas of a coal-fired power plant, we show that for some materials the heat requirement is reduced by as much as a factor of two using the correct heat capacity.

Representations of Materials for Machine Learning

“…When combined with compositional features [19], their representation results in better performance on predictions of formation enthalpy for ICSD than partial radial distribution functions [33] (Figure 1 in Reference [17]). In subsequent work, these descriptors have facilitated the prediction of experimental heat capacities in MOFs [34]. Similarly, Isayev et al [35] replaced faces of the Voronoi tessellation with virtual bonds and separated the resulting framework into sets of linear (up to four atoms) and shell-based (up to nearest neighbors) fragments.…”

Hierarchical Computational Screening of Quantum Metal–Organic Framework Database to Identify Metal–Organic Frameworks for Volatile Organic-Compound Capture from Air

“…The same group followed a similar approach using GCMC simulations to assess the potential of 31,399 hMOFs in capturing C 3 −C 4 alkanes from an air mixture mimicking VOC/N 2 /O 2 and calculated propane (C 3 H 8 ) selectivities between 10 −3 and 7.3 × 10 5 and butane (C 4 H 10 ) selectivities between 0.1 and 5.7 × 10 6 at 1 bar and 298 K. 32 The hybrid quantum-MOF database (QMOF, 20,375 MOFs), 33,34 which consists of experimental and hypothetical computation-ready MOFs collected from previously mentioned databases, has been recently introduced to offer a diverse collection of structurally optimized structures with a comprehensive range of chemical and physical properties. 22 This database was studied for predicting several properties of MOFs such as band gaps 33,35 and heat capacities, 36 but it has not been screened for a gas separation application to the best of our knowledge. Motivated by this, we aimed to screen this diverse material space for VOC capture and unlock the potential of QMOFs for adsorption-based C 4 H 10 separation from air for the first time in the literature.…”