Data-Driven Screening and Discovery of Metal-Organic Frameworks as C Adsorbents from over 900 Experimental Isotherms.

The separation of ethylene from ethane accounts for almost 100 million tons of CO emissions annually and 0.3% of global primary energy usage. Replacing current cryogenic distillation units with adsorption separation units, especially for the minor component of ethane, would enable significant efficiency gains.

Metal-Organic Framework Stability in Water and Harsh Environments from Data-Driven Models Trained on the Diverse WS24 Data Set.

Metal-organic frameworks (MOFs) are porous materials with applications in gas separations and catalysis, but a lack of water stability often limits their practical use given the ubiquity of water. Consequently, it is useful to predict whether a MOF is water-stable before investing time and resources into synthesis. Existing heuristics for designing water-stable MOFs lack generality and limit the diversity of explored chemistry due to narrowly defined criteria.

Visible light-mediated aza Paternò-Büchi reaction of acyclic oximes and alkenes to azetidines.

The aza Paternò-Büchi reaction is a [2+2]-cycloaddition reaction between imines and alkenes that produces azetidines, four-membered nitrogen-containing heterocycles. Currently, successful examples rely primarily on either intramolecular variants or cyclic imine equivalents. To unlock the full synthetic potential of aza Paternò-Büchi reactions, it is essential to extend the reaction to acyclic imine equivalents.

A Semi-Automated, High-Throughput Approach for the Synthesis and Identification of Highly Photo-Cytotoxic Iridium Complexes.

The discovery of new compounds with pharmacological properties is usually a lengthy, laborious and expensive process. Thus, there is increasing interest in developing workflows that allow for the rapid synthesis and evaluation of libraries of compounds with the aim of identifying leads for further drug development. Herein, we apply combinatorial synthesis to build a library of 90 iridium(III) complexes (81 of which are new) over two synthesise-and-test cycles, with the aim of identifying potential agents for photodynamic therapy.

Machine Learning Prediction of the Experimental Transition Temperature of Fe(II) Spin-Crossover Complexes.

Spin-crossover (SCO) complexes are materials that exhibit changes in the spin state in response to external stimuli, with potential applications in molecular electronics. It is challenging to know a priori how to design ligands to achieve the delicate balance of entropic and enthalpic contributions needed to tailor a transition temperature close to room temperature. We leverage the SCO complexes from the previously curated SCO-95 data set [Vennelakanti et al.

Active Learning Exploration of Transition-Metal Complexes to Discover Method-Insensitive and Synthetically Accessible Chromophores.

Transition-metal chromophores with earth-abundant transition metals are an important design target for their applications in lighting and nontoxic bioimaging, but their design is challenged by the scarcity of complexes that simultaneously have well-defined ground states and optimal target absorption energies in the visible region. Machine learning (ML) accelerated discovery could overcome such challenges by enabling the screening of a larger space but is limited by the fidelity of the data used in ML model training, which is typically from a single approximate density functional. To address this limitation, we search for consensus in predictions among 23 density functional approximations across multiple rungs of "Jacob's ladder".

Low-cost machine learning prediction of excited state properties of iridium-centered phosphors.

Prediction of the excited state properties of photoactive iridium complexes challenges methods such as time-dependent density functional theory (TDDFT) both from the perspective of accuracy and of computational cost, complicating high-throughput virtual screening (HTVS). We instead leverage low-cost machine learning (ML) models and experimental data for 1380 iridium complexes to perform these prediction tasks. We find the best-performing and most transferable models to be those trained on electronic structure features from low-cost density functional tight binding calculations.

MOFSimplify, machine learning models with extracted stability data of three thousand metal-organic frameworks.

We report a workflow and the output of a natural language processing (NLP)-based procedure to mine the extant metal-organic framework (MOF) literature describing structurally characterized MOFs and their solvent removal and thermal stabilities. We obtain over 2,000 solvent removal stability measures from text mining and 3,000 thermal decomposition temperatures from thermogravimetric analysis data. We assess the validity of our NLP methods and the accuracy of our extracted data by comparing to a hand-labeled subset.

Use of the Richtmyer-Meshkov instability to infer yield stress at high-energy densities.

We use the Richtmyer-Meshkov instability (RMI) at a metal-gas interface to infer the metal's yield stress (Y) under shock loading and release. We first model how Y stabilizes the RMI using hydrodynamics simulations with a perfectly plastic constitutive relation for copper (Cu). The model is then tested with molecular dynamics (MD) of crystalline Cu by comparing the inferred Y from RMI simulations with direct stress-strain calculations, both with MD at the same conditions.

Fastest growing linear Rayleigh-Taylor modes at solid/fluid and solid/solid interfaces.

Previous linear stability analyses of the Rayleigh-Taylor instability problem for elastic solids have been restricted to calculating the cutoff wavelength lambda(c) (zero growth rate) in the limit of Atwood number A of unity. Here, we rigorously derive the dispersion relations for solid/fluid and solid/solid interfaces and perform a systematic investigation to compute the most unstable modes (maximum growth rate) for all A. After rationalizing the dispersion relations into multivariable polynomials, we compute the physically meaningful wavelength lambda and growth rate sigma for all unstable disturbances as a function of the mechanical properties of the participating media (shear moduli, dynamic viscosity, and density contrast) and acceleration.