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.

How to Decarbonize Our Energy Systems: Process‐Informed Design of New Materials for Carbon Capture

“…Fig. 4 compares the outcomes of the PrISMa platform with a constant C p and with the C p obtained from the machine learning model of Moosavi et al [19]. Interestingly, for almost all materials, this gave a lower value of the energy requirements.…”

“…In addition, capture tests showed that this material outperformed the commercial materials. One of the interesting side-effects of the PrISMa platform is that we screen a little over thousand materials [19]. For these screenings, we used molecular simulations [20] to predict the pure component adsorption isotherms, and we used ideal adsorbed solution theory (IAST) to predict the mixture isotherms.…”

“…Hence, in our PrISMa platform, we assumed a constant value for all MOFs. This motivated us to develop a machine-learning approach to predict the heat capacity of the different porous materials (MOFs, zeolites, covalentorganic frameworks) [19]. Fig.…”

“…The right figure shows the correction factor. For some materials, we see the heat requirement has been reduced by a factor of two (figure based on data from Moosavi et al[19]).…”

Machine Learning Predicting Optimal Preparation of Silica-Coated Gold Nanorods for Photothermal Tumor Ablation

“…Recently, machine learning (ML) has emerged as a powerful tool of materials development for analyzing massive amounts of data, predicting physical and chemical properties of materials effectively, and establishing a constructive process–structure–property relationship [ 20 , 21 , 22 , 23 ]. Atwood et al reported that XGBoost is a powerful ML model for predicting the crystallization propensity of metal organic nanocapsules.…”