In this study, proton-conducting behaviors of a cerium-based metal–organic framework (MOF), Ce-MOF-808, its zirconium-based isostructural MOF, and bimetallic MOFs with various Zr-to-Ce ratios are investigated. The significantly increased proton conductivity (σ) and decreased activation energy (E a) are obtained by substituting Zr with Ce in the nodes of MOF-808. Ce-MOF-808 achieves a σ of 4.4 × 10–3 S/cm at 25 °C under 99% relative humidity and an E a of 0.14 eV; this value is among the lowest-reported E a of proton-conductive MOFs. Density functional theory calculations are utilized to probe the proton affinities of these MOFs. As the first study reporting the proton conduction in cerium-based MOFs, the finding here suggests that cerium-based MOFs should be a better platform for the design of proton conductors compared to the commonly reported zirconium-based MOFs in future studies on energy-related applications.

Metal-organic framework (MOF) in biomass valorization is a promising technology developed in recent decades. By tailoring both the metal nodes and organic ligands, MOFs exhibit multiple functionalities, which not only extend their applicability in biomass conversion but also increase the complexity of material designs. To address this issue, quantum mechanical simulations have been used to provide mechanistic insights into the catalysis of biomassderived molecules, which could potentially facilitate the development of novel MOF-based materials for biomass valorization. The aim of this review is to survey recent quantum mechanical simulations on biomass reactions occurring in MOF catalysts, with the emphasis on the studies of the catalytic activity of active sites and the effects of organic ligand and porous structures on the kinetics. Moreover, different model systems and computational methods used for MOF simulations are also surveyed and discussed in this review.

A closed-loop, autonomous molecular discovery platform driven by integrated machine learning tools was developed to accelerate the design of molecules with desired properties. Two case studies are demonstrated on dye-like molecules, targeting absorption wavelength, lipophilicity, and photo-oxidative stability. In the first, the platform experimentally realized 312 unreported molecules across three automatic iterations of molecular design-make-test-analyze cycles while exploring the structure–function space of four rarely reported scaffolds. In each iteration, the property-prediction models which guided the exploration learned the structure–property space of diverse inexpensive scaffold derivatives realized through using multi-step syntheses. Conversely, the second study exploited property models trained on a chemical space with pre-existing examples to discover 6 top-performing molecules within the structure-property space. By closing the molecular discovery cycle of prediction, synthesis, measurement, and model retraining, the platform demonstrates the potential for integrated platforms to automatically understand a local chemical space and discover functional molecules.

Deep learning has become a powerful and frequently employed tool for the prediction of molecular properties, thus creating a need for open-source and versatile software solutions that can be operated by non-experts. Among current approaches, directed message-passing neural networks (D-MPNNs) have proven to perform well on a variety of property prediction tasks. The software package Chemprop implements the D-MPNN architecture, and offers simple, easy, and fast access to machine-learned molecular properties. Compared to its initial version, we present a multitude of new Chemprop functionalities such as the support of multi-molecule properties, reactions, atom/bond-level properties, and spectra. Further, we incorporate various uncertainty quantification and calibration methods along with related metrics, as well as pretraining and transfer learning workflows, improved hyperparameter optimization, and other customization options concerning loss functions or atom/bond features. We benchmark D-MPNN models trained using Chemprop with the new reaction, atom-level and spectra functionality on a variety of property prediction datasets, including MoleculeNet and SAMPL, and observe state-of-the-art performance on the prediction of water-octanol partition coefficients, reaction barrier heights, atomic partial charges, and absorption spectra. Chemprop enables out-of-the-box training of D-MPNN models for a variety of problem settings in a fast, user-friendly, and open-source software.