To mimic the photosynthetic reactions in artificial photosynthetic systems, the following should be accomplished first:
1) Finding the appropriate compromise between the complexity of the biological system and a simplified structure of an artificial photosynthetic system, taking into account the crucial elements of the active site.
2) To sample parameter space in an economical way to find the most rapid convergence to desired systems and to overcome the time scale limitations in the simulations.
3) Improve the accuracy and efficiency in computing excited-state potential energy surfaces.
4) Develop new computational strategies, specifically, grid-based computing methods for they provide seamless and scalable access to wide-area distributed resources and they are particularly suitable in the implementation of evolutionary algorithms.
5) Develop a rational compound design technique which would allow to avoid screening of the high-dimensional chemical space spanned by all the possible combination's and configurations of electrons and nuclei and to perform a gradual optimization of the chemical structure of a compound using grand-canonical density functional methods.
6) Develop an adaptive technique which would let us study large systems: a part of the system (e.g. next to the electrodes) can be treated quantum mechanically, the nearest layer atomistically, and the bulk of the system has a coarse-grained description.
There is the potential to develop dedicated systems, whether based on cyanobacteria, plants, current biofuel producing crops or artificial components, capable of much higher efficiencies, reaching 10% efficiency of solar energy conversion. This would enable enough energy and fuel to be produced for a large part of the planet's needs without causing significant loss of space for food production.
The current generation of biofuel producing crops generally convert less than 1% of the solar energy they receive to biomass, which means they would displace too much agricultural land used for food production to be viable on a large scale.
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