The choice of metal and linker together define the structure and therefore the guest accessibility of a metal‐organic framework (MOF), but the large number of possible metal‐linker combinations makes the selection of components for synthesis challenging. We predict the guest accessibility of a MOF with 80.5 % certainty based solely on the identity of these two components as chosen by the experimentalist, by decomposing reported experimental three‐dimensional MOF structures in the Cambridge Structural Database into metal and linker and then learning the connection between the components’ chemistry and the MOF porosity. Pore dimensions of the guest‐accessible space are classified into four ranges with three sequential models. Both the dataset and the predictive models are available to download and offer simple guidance in prioritization of the choice of the components for exploratory MOF synthesis for separation and catalysis based on guest accessibility considerations.

Magneto‐caloric materials offer the possibility to design environmentally friendlier thermal management devices compared to the widely used gas‐based systems. The challenges to develop this solid‐state based technology lie in the difficulty of finding materials presenting a large magneto‐caloric effect over a broad temperature span together with suitable secondary application parameters such as low heat capacity and high thermal conductivity. A series of compounds derived from the PbFCl structure is investigated using a combination of computational and experimental methods focusing on the change of cell volume in magnetic and non‐magnetic ground states. Scaling analysis of the magnetic properties determines that they are second order phase transition ferromagnets and that the magnetic entropy change is driven by the coupling of magneto‐elastic strain in the square‐net through the magnetic transition determined from neutron and synchrotron X‐ray diffraction. The primary and secondary application related properties are measured experimentally, and the c/a parameter is identified as an accurate proxy to control the magnetic transition. Chemical substitution on the square‐net affords tuning of the Curie temperature over a broad temperature span between 252 and 322 K. A predictive machine learning model for the c/a parameter is developed to guide future exploratory synthesis.

The choice of metal and linker together define the structure and therefore the guest accessibility of a metal‐organic framework (MOF), but the large number of possible metal‐linker combinations makes the selection of components for synthesis challenging. We predict the guest accessibility of a MOF with 80.5 % certainty based solely on the identity of these two components as chosen by the experimentalist, by decomposing reported experimental three‐dimensional MOF structures in the Cambridge Structural Database into metal and linker and then learning the connection between the components’ chemistry and the MOF porosity. Pore dimensions of the guest‐accessible space are classified into four ranges with three sequential models. Both the dataset and the predictive models are available to download and offer simple guidance in prioritization of the choice of the components for exploratory MOF synthesis for separation and catalysis based on guest accessibility considerations.

The discovery of new materials often requires collaboration between experimental and computational chemists. Web based platforms allow more flexibility in this collaboration by giving access to computational tools without the need for access to computational researchers. We present Liverpool Materials Discovery Server (lmds.liverpool.ac.uk), one such platform which currently hosts six state of the art computational tools in an easy to use format. We describe the development of this platform, highlighting the advantages and disadvantages the methods used. In addition, we provide source code, and setup scripts to enable other research groups to create similar platforms, to promote collaboration both within and between research groups.

With machine learning being a popular topic in current computational materials science literature, creating representations for compounds has become common place. These representations are rarely compared, as evaluating their performance...