Strength and Nature of Host‐Guest Interactions in Metal‐Organic Frameworks from a Quantum‐Chemical Perspective

Metal-organic frameworks (MOFs) offer a convenient means for capturing, transporting, and releasing small molecules. Their rational design requires an in-depth understanding of the underlying non-covalent host-guest interactions, and the ability to easily and rapidly pre-screen candidate architectures in silico. In this work, we devised a recipe for computing the strength and analysing the nature of the host-guest interactions in MOFs. By assessing a range of density functional theory methods across periodic and finite supramolecular cluster scale we find that appropriately constructed clusters readily reproduce the key interactions occurring in periodic models at a fraction of the computational cost. Host-guest interaction energies can be reliably computed with dispersion-corrected density functional theory methods; however, decoding their precise nature demands insights from energy decomposition schemes and quantum-chemical tools for bonding analysis such as the quantum theory of atoms in molecules, the non-covalent interactions index or the density overlap regions indicator

Engineering Host–Guest Interactions in Organic Framework Materials for Drug Delivery

Metal-organic frameworks (MOF) and covalent organic frameworks (COFs) are promising nanocarriers for targeted drug delivery. Noncovalent interactions between frameworks and drugs play a fundamental role in the therapeutic uptake and release of the latter. However, the scope of framework functionalizations and deliverable drugs remains underexplored. Using a multilevel approach combining molecular docking and density functional theory, we show for a range of drugs and frameworks that experimentally reported release metrics are in good agreement with the in silico computed host–guest interaction energies. Functional groups within the framework significantly impact the strength of these host–guest interactions, while a given framework can serve as an efficient delivery agent for drugs beyond the prototypical few. Our findings identify the interaction energy as a reliable and relatively easy to compute descriptor of organic framework materials for drug delivery, able to facilitate their high-throughput screening and targeted design towards extended-release times

Sensing and sensitivity:Computational chemistry of graphene‐based sensors

Highly efficient, tunable, biocompatible, and environmentally friendly electrochemical sensors featuring graphene-based materials pose a formidable challenge for computational chemistry. In silico rationalization, optimization and, ultimately, prediction of their performance requires exploring a vast structural space of potential surface-analyte complexes, further complicated by the presence of various defects and functionalities within the infinite graphene lattice. This immense number of systems and their periodic nature greatly limit the choice of computational tools applicable at a reasonable cost. An alternative approach using finite nanoflake models opens the doors to many more advanced and accurate electronic structure methods, while sacrificing the realism of representation. Locating the surface-analyte complex is followed by an in-depth in silico analysis of its energetic and electronic properties using, for example, energy decomposition schemes, as well as simulation of the signal, for example, a zero-bias transmission spectra or a current–voltage curve, by means of the nonequilibrium Green's function method. These and other properties are examined in the context of a sensor's selectivity, sensitivity, and limit of detection with an aim to establish design principles for future devices. Herein, we analyze the advantages and limitations of diverse computational chemistry methods used at each of these steps in simulating graphene-based electrochemical sensors. We present outstanding challenges toward predictive models and sketch possible solutions involving such contemporary techniques as multiscale simulations and high-throughput screening

Host–guest interactions in framework materials:Insight from modeling

The performance of metal–organic and covalent organic framework materials in sought-after applications—capture, storage, and delivery of gases and molecules, and separation of their mixtures—heavily depends on the host–guest interactions established inside the pores of these materials. Computational modeling provides information about the structures of these host–guest complexes and the strength and nature of the interactions present at a level of detail and precision that is often unobtainable from experiment. In this Review, we summarize the key simulation techniques spanning from molecular dynamics and Monte Carlo methods to correlate ab initio approaches and energy, density, and wavefunction partitioning schemes. We provide illustrative literature examples of their uses in analyzing and designing organic framework hosts. We also describe modern approaches to the high-throughput screening of thousands of existing and hypothetical metal–organic frameworks (MOFs) and covalent organic frameworks (COFs) and emerging machine learning techniques for predicting their properties and performances. Finally, we discuss the key methodological challenges on the path toward computation-driven design and reliable prediction of high-performing MOF and COF adsorbents and catalysts and suggest possible solutions and future directions in this exciting field of computational materials science

Substituting density functional theory in reaction barrier calculations for hydrogen atom transfer in proteins

Hydrogen atom transfer (HAT) reactions are important in many biological systems. As these reactions are hard to observe experimentally, it is of high interest to shed light on them using simulations. Here, we present a machine learning model based on graph neural networks for the prediction of energy barriers of HAT reactions in proteins. As input, the model uses exclusively non-optimized structures as obtained from classical simulations. It was trained on more than 17 000 energy barriers calculated using hybrid density functional theory. We built and evaluated the model in the context of HAT in collagen, but we show that the same workflow can easily be applied to HAT reactions in other biological or synthetic polymers. We obtain for relevant reactions (small reaction distances) a model with good predictive power (R2 ∼ 0.9 and mean absolute error of <3 kcal mol−1). As the inference speed is high, this model enables evaluations of dozens of chemical situations within seconds. When combined with molecular dynamics in a kinetic Monte-Carlo scheme, the model paves the way toward reactive simulations

Understanding and exploiting interfacial interactions between phosphonic acid functional groups and co-evaporated perovskites

Interfacial engineering has fueled recent development of p-i-n perovskite solar cells (PSCs), with self-assembled monolayer-based hole-transport layers (SAM-HTLs) enabling almost lossless contacts for solution-processed PSCs, resulting in the highest achieved power conversion efficiency (PCE) to date. Substrate interfaces are particularly crucial for the growth and quality of co-evaporated PSCs. However, adoption of SAM-HTLs for co-evaporated perovskite absorbers is complicated by the underexplored interaction of such perovskites with phosphonic acid functional groups. In this work, we highlight how exposed phosphonic acid functional groups impact the initial phase and final bulk crystal structures of co-evaporated perovskites and their resultant PCE. The explored surface interaction is mediated by hydrogen bonding with interfacial iodine, leading to increased formamidinium iodide adsorption, persistent changes in perovskite structure, and stabilization of bulk α-FAPbI3, hypothesized as being due to kinetic trapping. Our results highlight the potential of exploiting substrates to increase control of co-evaporated perovskite growth

Fine-Tuned Organic Photoredox Catalysts for Fragmentation-Alkynylation Cascades of Cyclic Oxime Ethers

Fine-tuned organic photoredox catalysts are introduced for the metal-free alkynylation of alkylnitrile radicals generated via oxidative ring opening of cyclic alkylketone oxime ethers.</p

Heterotetracenes: Flexible Synthesis and in Silico Assessment of the Hole Transport Properties

Thienoacenes and furoacenes are among the most frequent molecular units found in organic materials. The efficient synthesis of morphologically different heteroacenes and the rapid determination of their solid-state and electronic properties are still challenging tasks, which slow down progress in the development of new materials. Herein, we report a flexible and efficient synthesis of unprecedented heterotetracenes based on a platinum- and gold- catalyzed cyclization-alkynylation domino process using EthynylBenziodoXole (EBX) hypervalent iodine reagents as key step. The proof-of-principle in silico estimation of the synthesized tetracenes' charge transport properties reveals their strong dependence on both the position and nature of the heteroatoms in the ring system. A broad range of mobilities is predicted, with some compounds displaying performance potentially comparable to that of state-of-the art electronic organic materials

Accelerated chemical science with AI

In light of the pressing need for practical materials and molecular solutions to renewable energy and health problems, to name just two examples, one wonders how to accelerate research and development in the chemical sciences, so as to address the time it takes to bring materials from initial discovery to commercialization. Artificial intelligence (AI)-based techniques, in particular, are having a transformative and accelerating impact on many if not most, technological domains. To shed light on these questions, the authors and participants gathered in person for the ASLLA Symposium on the theme of ‘Accelerated Chemical Science with AI’ at Gangneung, Republic of Korea. We present the findings, ideas, comments, and often contentious opinions expressed during four panel discussions related to the respective general topics: ‘Data’, ‘New applications’, ‘Machine learning algorithms’, and ‘Education’. All discussions were recorded, transcribed into text using Open AI's Whisper, and summarized using LG AI Research's EXAONE LLM, followed by revision by all authors. For the broader benefit of current researchers, educators in higher education, and academic bodies such as associations, publishers, librarians, and companies, we provide chemistry-specific recommendations and summarize the resulting conclusions