Final Report Summary - ROBOT (Robust Organic Tectonics)
Functional molecular crystals are important in everyday life and the properties of these materials is inherently linked to their structure. However, predicting the structure of functional molecular crystals, or the preference of one crystalline form over another, is far from routine. This is because structure-energy landscapes are rarely dominated by a single intermolecular interaction. Instead, crystal structures result from the balance of many weak interactions. The makes functional design difficult because the accurate prediction of functional properties is depends on access to reliable structural information. The ambitious goal of the RobOT project was to introduce a new level of ‘designability’ into functional molecular crystals by integrating computation with experimentation. The aim of this approach was accelerate materials discovery by targeting materials with specific functions, which could be predicted ab initio using simple sketches of a target molecules as the only input. The RobOT project focussed on demonstrating this in the field of porous molecular crystals – an alternative to porous extended frameworks, such as zeolites, metal-organic frameworks, and polymer networks.
During the project, we developed a method to build energy–structure–function maps that describe the possible structures and properties that are available to a candidate molecule. Using these maps, we identified a highly porous solid that had the lowest density reported for a molecular crystal. This work was published in Nature (2017, 543, 657-664). In this study, we demonstrated that the structure of these crystals and their physical properties, such as methane storage capacity and guest-molecule selectivity, could be accurately predicted using the molecular structure as the only input. We found not one but five forms of a molecule, T2, all of which were predicted a priori.
This integration of computational prediction with experimental synthesis and characterisation is the key achievement of the RobOT project. This also relates to a study reported in Nature Materials “Separation of rare gases and chiral molecules by selective binding in porous organic cages” (Nature Materials, 2014, 13, 954-960), which was underpinned by simulation and diffraction work carried out as part of the RobOT project. This study represented the successful integration of molecular selectivity predictions with experiment to develop an organic molecular material for a potential real-world application. We have also filed a patent application for this technology in 2014, and are evaluating possible industrial partnerships and marketing to potential licensees.
During the project, we developed a method to build energy–structure–function maps that describe the possible structures and properties that are available to a candidate molecule. Using these maps, we identified a highly porous solid that had the lowest density reported for a molecular crystal. This work was published in Nature (2017, 543, 657-664). In this study, we demonstrated that the structure of these crystals and their physical properties, such as methane storage capacity and guest-molecule selectivity, could be accurately predicted using the molecular structure as the only input. We found not one but five forms of a molecule, T2, all of which were predicted a priori.
This integration of computational prediction with experimental synthesis and characterisation is the key achievement of the RobOT project. This also relates to a study reported in Nature Materials “Separation of rare gases and chiral molecules by selective binding in porous organic cages” (Nature Materials, 2014, 13, 954-960), which was underpinned by simulation and diffraction work carried out as part of the RobOT project. This study represented the successful integration of molecular selectivity predictions with experiment to develop an organic molecular material for a potential real-world application. We have also filed a patent application for this technology in 2014, and are evaluating possible industrial partnerships and marketing to potential licensees.