Computational chemistry provides a versatile toolbox for studying mechanistic details of catalytic reactions and holds promise to deliver practical strategies to enable the rational in silico catalyst design. The versatile reactivity and nontrivial electronic structure effects, common for systems based on 3d transition metals, introduce additional complexity that may represent a particular challenge to the standard computational strategies. In this review, we discuss the challenges and capabilities of modern electronic structure methods for studying the reaction mechanisms promoted by 3d transition metal molecular catalysts. Particular focus will be placed on the ways of addressing the multiconfigurational problem in electronic structure calculations and the role of expert bias in the practical utilization of the available methods. The development of density functionals designed to address transition metals is also discussed. Special emphasis is placed on the methods that account for solvation effects and the multicomponent nature of practical catalytic systems. This is followed by an overview of recent computational studies addressing the mechanistic complexity of catalytic processes by molecular catalysts based on 3d metals. Cases that involve noninnocent ligands, multicomponent reaction systems, metal–ligand and metal–metal cooperativity, as well as modeling complex catalytic systems such as metal–organic frameworks are presented. Conventionally, computational studies on catalytic mechanisms are heavily dependent on the chemical intuition and expert input of the researcher. Recent developments in advanced automated methods for reaction path analysis hold promise for eliminating such human-bias from computational catalysis studies. A brief overview of these approaches is presented in the final section of the review. The paper is closed with general concluding remarks.

Utilization of self‐healing chemistry to develop synthetic polymer materials that can heal themselves with restored mechanical performance and functionality is of great interest. Self‐healable polymer elastomers with tunable mechanical properties are especially attractive for a variety of applications. Herein, a series of urea functionalized poly(dimethyl siloxane)‐based elastomers (U‐PDMS‐Es) are reported with extremely high stretchability, self‐healing mechanical properties, and recoverable gas‐separation performance. Tailoring the molecular weights of poly(dimethyl siloxane) or weight ratio of elastic cross‐linker offers tunable mechanical properties of the obtained U‐PDMS‐Es, such as ultimate elongation (from 984% to 5600%), Young's modulus, ultimate tensile strength, toughness, and elastic recovery. The U‐PDMS‐Es can serve as excellent acoustic and vibration damping materials over a broad range of temperature (over 100 °C). The strain‐dependent elastic recovery behavior of U‐PDMS‐Es is also studied. After mechanical damage, the U‐PDMS‐Es can be healed in 120 min at ambient temperature or in 20 min at 40 °C with completely restored mechanical performance. The U‐PDMS‐Es are also demonstrated to exhibit recoverable gas‐separation functionality with retained permeability/selectivity after being damaged.

Machine learning and high-throughput computational screening have been valuable tools in accelerated first-principles screening for the discovery of the next generation of functionalized molecules and materials. The application of machine learning for chemical applications requires the conversion of molecular structures to a machine-readable format known as a molecular representation. The choice of such representations impacts the performance and outcomes of chemical machine learning methods. Herein, we present a new concise molecular representation derived from persistent homology, an applied branch of mathematics. We have demonstrated its applicability in a high-throughput computational screening of a large molecular database (GDB-9) with more than 133,000 organic molecules. Our target is to identify novel molecules that selectively interact with CO 2. The methodology and performance of the novel molecular fingerprinting method is presented and the new chemicallydriven persistence image representation is used to screen the GDB-9 database to suggest molecules and/or functional groups with enhanced properties.

Solving the coupled-cluster (CC) equations is a cost-prohibitive process that exhibits poor scaling with system size. These equations are solved by determining the set of amplitudes (t) that minimize the system energy with respect to the coupled-cluster equations at the selected level of truncation. Here, a novel approach to predict the converged coupled-cluster singles and doubles (CCSD) amplitudes, thus the coupled-cluster wave function, is explored by using machine learning and electronic structure properties inherent to the MP2 level. Features are collected from quantum chemical data, such as orbital energies, one-electron Hamiltonian, Coulomb, and exchange terms. The data-driven CCSD (DDCCSD) is not an alchemical method because the actual iterative coupled-cluster equations are solved. However, accurate energetics can also be obtained by bypassing solving the CC equations entirely. Our preliminary data show that it is possible to achieve remarkable speedups in solving the CCSD equations, especially when the correct physics are encoded and used for training of machine learning models.

Three five-coordinate iron(IV) imide complexes have been synthesized and characterized.T hese novel structures have disparate spin states on the iron as afunction of the R-group attached to the imide,w ith alkylg roups leading to low-spin diamagnetic (S = 0) complexes and an aryl group leading to an intermediate-spin (S = 1) complex. The different spin states lead to significant differences in the bonding about the iron center as well as the spectroscopic properties of these complexes.M çssbauer spectroscopyc onfirmed that all three imide complexes are in the iron(IV) oxidation state.T he combination of diamagnetism and 15 Nlabeling allowed for the first 15 NN MR resonance recorded on an iron imide.M ultireference calculations corroborate the experimental structural findings and suggest how the bonding is distinctly different on the imide ligand between the two spin states.Supportinginformation and the ORCID identification number(s) for the author(s) of this article can be found under: https://doi.