Designing building blocks of covalent organic frameworks through on-the-fly batch-based Bayesian optimization.

In this work, we use a Bayesian optimization (BO) algorithm to sample the space of covalent organic framework (COF) components aimed at the design of COFs with a high hole conductivity. COFs are crystalline, often porous coordination polymers, where organic molecular units-called building blocks (BBs)-are connected by covalent bonds. Even though we limit ourselves here to a space of three-fold symmetric BBs forming two-dimensional COF sheets, their design space is still much too large to be sampled by traditional means through evaluating the properties of each element in this space from first principles.

Correction: Photoelectron angular distributions as sensitive probes of surfactant layer structure at the liquid-vapor interface.

Correction for 'Photoelectron angular distributions as sensitive probes of surfactant layer structure at the liquid-vapor interface' by Rémi Dupuy , , 2022, , 4796-4808, https://doi.org/10.1039/D1CP05621B.

Interpreting ultrafast electron transfer on surfaces with a converged first-principles Newns-Anderson chemisorption function.

We study the electronic coupling between an adsorbate and a metal surface by calculating tunneling matrix elements Had directly from first principles. For this, we employ a projection of the Kohn-Sham Hamiltonian upon a diabatic basis using a version of the popular projection-operator diabatization approach. An appropriate integration of couplings over the Brillouin zone allows the first calculation of a size-convergent Newns-Anderson chemisorption function, a coupling-weighted density of states measuring the line broadening of an adsorbate frontier state upon adsorption.

Ångstrom-Depth Resolution with Chemical Specificity at the Liquid-Vapor Interface.

The determination of depth profiles across interfaces is of primary importance in many scientific and technological areas. Photoemission spectroscopy is in principle well suited for this purpose, yet a quantitative implementation for investigations of liquid-vapor interfaces is hindered by the lack of understanding of electron-scattering processes in liquids. Previous studies have shown, however, that core-level photoelectron angular distributions (PADs) are altered by depth-dependent elastic electron scattering and can, thus, reveal information on the depth distribution of species across the interface.

Machine Learning Enabled Image Analysis of Time-Temperature Sensing Colloidal Arrays.

Smart, responsive materials are required in various advanced applications ranging from anti-counterfeiting to autonomous sensing. Colloidal crystals are a versatile material class for optically based sensing applications owing to their photonic stopband. A careful combination of materials synthesis and colloidal mesostructure rendered such systems helpful in responding to stimuli such as gases, humidity, or temperature.