The search for the ideal biocatalyst

While the use of enzymes as biocatalysts to assist in the industrial manufacture of fine chemicals and pharmaceuticals has enormous potential, application is frequently limited by evolution-led catalyst traits. The advent of designer biocatalysts, produced by informed selection and mutation through recombinant DNA technology, enables production of process-compatible enzymes. However, to fully realize the potential of designer enzymes in industrial applications, it will be necessary to tailor catalyst properties so that they are optimal not only for a given reaction but also in the context of the industrial process in which the enzyme is applied

Understanding Novel Superconductors with Ab Initio Calculations

This chapter gives an overview of the progress in the field of computational superconductivity. Following the MgB2 discovery (2001), there has been an impressive acceleration in the development of methods based on Density Functional Theory to compute the critical temperature and other physical properties of actual superconductors from first-principles. State-of-the-art ab-initio methods have reached predictive accuracy for conventional (phonon-mediated) superconductors, and substantial progress is being made also for unconventional superconductors. The aim of this chapter is to give an overview of the existing computational methods for superconductivity, and present selected examples of material discoveries that exemplify the main advancements.Comment: 38 pages, 10 figures, Contribution to Springer Handbook of Materials Modellin

Functional materials discovery using energy–structure–function maps

Molecular crystals cannot be designed in the same manner as macroscopic objects, because they do not assemble according to simple, intuitive rules. Their structures result from the balance of many weak interactions, rather than from the strong and predictable bonding patterns found in metal–organic frameworks and covalent organic frameworks. Hence, design strategies that assume a topology or other structural blueprint will often fail. Here we combine computational crystal structure prediction and property prediction to build energy–structure–function maps that describe the possible structures and properties that are available to a candidate molecule. Using these maps, we identify a highly porous solid, which has the lowest density reported for a molecular crystal so far. Both the structure of the crystal and its physical properties, such as methane storage capacity and guest-molecule selectivity, are predicted using the molecular structure as the only input. More generally, energy–structure–function maps could be used to guide the experimental discovery of materials with any target function that can be calculated from predicted crystal structures, such as electronic structure or mechanical properties