Efficient Exploration of Adsorption Space for Separations in Metal–Organic Frameworks Combining the Use of Molecular Simulations, Machine Learning, and Ideal Adsorbed Solution Theory
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37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Engineering topics
Tang, Dai
Publications and source records attributed to Tang, Dai.
Two-Dimensional Energy Histograms as Features for Machine Learning to Predict Adsorption in Diverse Nanoporous Materials
A major obstacle for machine learning (ML) in chemical science is the lack of physically informed feature representations that provide both accurate prediction and easy interpretability of the ML model. In this work, we describe adsorption systems using novel two-dimensional energy histogram (2D-EH) features, which are obtained from the probe-adsorbent energies and energy gradients at grid points located throughout the adsorbent. The 2D-EH features encode both energetic and structural information of the material and lead to highly accurate ML models (coefficient of determination R2 ~ 0.94–0.99) for predicting single-component adsorption capacity in metal–organic frameworks (MOFs). Here, we consider the adsorption of spherical molecules (Kr and Xe), linear alkanes with a wide range of aspect ratios (ethane, propane, n-butane, and n-hexane), and a branched alkane (2,2-dimethylbutane) over a wide range of temperatures and pressures. The interpretable 2D-EH features enable the ML model to learn the basic physics of adsorption in pores from the training data. We show that these MOF-data-trained ML models are transferrable to different families of amorphous nanoporous materials. We also identify several adsorption systems where capillary condensation occurs, and ML predictions are more challenging. Nevertheless, our 2D-EH features still outperform structural features including those derived from persistent homology. The novel 2D-EH features may help accelerate the discovery and design of advanced nanoporous materials using ML for gas storage and separation in the future.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Metal–Organic Framework Integrating Ionic Framework and Bimetallic Coupling Effect for Highly Efficient Oxygen Evolution Reaction
Metal–organic frameworks (MOFs) are recognized as promising electrocatalysts for the oxygen evolution reaction (OER) because of their permanent porosity and rich architectural diversity; however, ionic MOFs enabling fast ions exchange during OER are rarely explored. Here, an ionic MOF (Ni-btz) constructed with an azolate ligand is selected, and continuous 3D bimetallic MOF (NiFe-btz) films deriving from high-degree intergrowth of microsized MOFs particles are fabricated. The as-prepared NiFe-btz/NF-OH electrode exhibits excellent OER performance with a low overpotential of 239 mV at 10 mA cm –2 under alkaline condition. The OER charge transfer process and bimetallic coupling effect in ionic NiFe-btz are probed by density functional theory calculations and confirmed via X-ray photoelectron spectroscopy and in situ Raman measurements. The partial density of states of NiFe-btz indicates that the main contribution for electron density around the Fermi level is from Cl ions clarifying the profitable impact of ionic MOF framework. This work systematically demonstrates the relationship of electronic structure and OER activity in ionic, bimetallic MOFs and expands the scope of 3D MOF films for efficient OER.
36 MATERIALS SCIENCE↗