OGRe: Optimal Grid Refinement Protocol for Accurate Free Energy Surfaces and Its Application in Proton Hopping in Zeolites and 2D COF Stacking

While free energy surfaces are the crux of our understanding of many chemical and biological processes, their accuracy is generally unknown. Moreover, many developments to improve their accuracy are often complicated, limiting their general use. Luckily, several tools and guidelines are already in place to identify these shortcomings, but they are typically lacking in flexibility or fail to systematically determine how to improve the accuracy of the free energy calculation. To overcome these limitations, this work introduces OGRe, a Python package for optimal grid refinement in an arbitrary number of dimensions. OGRe is based on three metrics that gauge the confinement, consistency, and overlap of each simulation in a series of umbrella sampling (US) simulations, an enhanced sampling technique ubiquitously adopted to construct free energy surfaces for hindered processes. As these three metrics are fundamentally linked to the accuracy of the weighted histogram analysis method adopted to generate free energy surfaces from US simulations, they facilitate the systematic construction of accurate free energy profiles, where each metric is driven by a specific umbrella parameter. This allows for the derivation of a consistent and optimal collection of umbrellas for each simulation, largely independent of the initial values, thereby dramatically increasing the ease-of-use toward accurate free energy surfaces. As such, OGRe is particularly suited to determine complex free energy surfaces with large activation barriers and shallow minima, which underpin many physical and chemical transformations and hence to further our fundamental understanding of these processes

On the Thermodynamics of Framework Breathing: A Free Energy Model for Gas Adsorption in MIL-53

When adsorbing guest molecules, the porous metal–organic framework MIL-53­(Cr) may vary its cell parameters drastically while retaining its crystallinity. A first approach to the thermodynamic analysis of this “framework breathing” consists of comparing the osmotic potential in two distinct shapes only (large-pore and narrow-pore). In this paper, we propose a generic parametrized free energy model including three contributions: host free energy, guest–guest interactions, and host–guest interaction. Free energy landscapes may now be constructed scanning all shapes and any adsorbed amount of guest molecules. This allows us to determine which shapes are the most stable states for arbitrary combinations of experimental control parameters, such as the adsorbing gas chemical potential, the external pressure, and the temperature. The new model correctly reproduces the structural transitions along the CO₂ and CH₄ isotherms. Moreover, our model successfully explains the adsorption versus desorption hysteresis as a consequence of the creation, stabilization, destabilization, and disappearance of a second free energy minimum under the assumptions of a first-order phase transition and collective behavior. Our general thermodynamic description allows us to decouple the gas chemical potential μ and mechanical pressure <i>P</i> as two independent thermodynamic variables and predict the complete (μ, <i>P</i>) phase diagram for CO₂ adsorption in MIL-53­(Cr). The free energy model proposed here is an important step toward a general thermodynamics description of flexible metal–organic frameworks

High-Throughput Screening of Covalent Organic Frameworks for Carbon Capture Using Machine Learning

Postcombustion carbon capture provides a high-potential pathway to reduce anthropogenic CO2 emissions in the short term. In this respect, nanoporous materials, such as covalent organic frameworks (COFs), offer a promising platform as adsorbent beds. However, due to the modular nature of COFs, an almost unlimited number of structures can possibly be synthesized. To efficiently identify promising materials and reveal performance trends within the COF material space, we present a computational high-throughput screening of 268,687 COFs for their ability to efficiently and selectively separate CO2 from the flue gas of power plants using a pressure swing adsorption process. Furthermore, we demonstrate that our screening can be significantly accelerated using machine learning to identify a set of promising materials. These are subsequently characterized with high accuracy, taking into account the effects of competitive adsorption and coadsorption. Our screening reveals that imide, (keto)enamine, and (acyl)hydrazone COFs are particularly interesting for carbon capture. Additionally, the best-performing materials are 3D COFs possessing strong CO2 adsorption sites between aromatic rings at opposite sides of pores with a diameter of 1.0 nm. In 2D COFs, a significant influence of the framework chemistry, such as imide linkages or fluoro groups, is observed. Our design rules can guide experimental researchers to construct high-performing COFs for CO2 capture