Building Machine Learning Small Molecule Melting Points and Solubility Models Using CCDC Melting Points Dataset.

Predicting solubility of small molecules is a very difficult undertaking due to the lack of reliable and consistent experimental solubility data. It is well known that for a molecule in a crystal lattice to be dissolved, it must, first, dissociate from the lattice and then, second, be solvated. The melting point of a compound is proportional to the lattice energy, and the octanol-water partition coefficient (log P) is a measure of the compound's solvation efficiency. The CCDC's melting point dataset of almost one hundred thousand compounds was utilized to create widely applicable machine learning models of small molecule melting points. Using the general solubility equation, the aqueous thermodynamic solubilities of the same compounds can be predicted. The global model could be easily localized by adding additional melting point measurements for a chemical series of interest.

CSD Communications of the Cambridge Structural Database.

The Cambridge Structural Database (CSD) is a collection of over one million experimental three-dimensional structures obtained through crystallographic analyses. These structures are determined by crystallographers worldwide and undergo curation and enhancement by scientists at the Cambridge Crystallographic Data Centre (CCDC) prior to their addition to the database. Though the CSD is substantial and contains widespread chemical diversity across organic and metal-organic compounds, it is estimated that a significant proportion of crystal structures determined are not published or shared through the peer-reviewed journal mechanism. To help overcome this, scientists can publish structures directly through the database as CSD Communications and these structural datasets are made publicly available alongside structures associated with scientific articles. CSD Communications contribute to the collective crystallographic knowledge as nearly two thirds are novel structures that are not otherwise available in the scientific literature. The primary benefits of sharing data through CSD Communications include the long-term preservation of scientific data, the strengthening of a widely data-mined world repository (the CSD), and the opportunity for scientists to receive recognition for their work through a formal and citable data publication. All CSD Communications are assigned unique digital object identifiers (DOIs). Contributions as CSD Communications currently comprise about 3.89% of the total CSD entries. Each individual CSD Communication is free to view and retrieve from the CCDC website.

Photomagnetic studies on spin-crossover solid solutions containing two different metal complexes, [Fe(1-bpp)(2)](x)[M(terpy)2](1-x)[BF4]2 (M = Ru or Co).

The photomagnetic properties of two series of spin-crossover solid solutions, [Fe(1-bpp)(2)](x)[Ru(terpy)(2)](1-x)(BF(4))(2) and [Fe(1-bpp)(2)](x)[Co(terpy)(2)](1-x)(BF(4))(2) (1-bpp = 2,6-bis[pyrazol-1-yl]pyridine), have been investigated. For all the materials, the evolution of the T(LIESST) value, the high-spin → low-spin relaxation parameters and the LITH loops were thoroughly studied. Interestingly in the Fe:Co series, along the photo-excitation, cobalt ions are concomitantly converted from low-spin to high-spin states with the iron centres, and also fully relax after light excitation.