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Nanoscale Simulations
 
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Linear scaling DFT

Linear scaling DFT brings an alternate perspective, and effectively allows for describing thousands of atoms[11] at a QM level. This efficiency is achieved employing algorithms with a cost that grows linearly with system size. In the past years, I have driven development of CP2K/Quickstep, with efficient and linear scaling algorithms and new methods.[11, 19, 29, 33, 34] I have demonstrated that systems of nano-size scale (Fig. 2) can now be simulated with ease and on a routine basis. A large part of the effort has been dedicated to guarantee that this can be done without sacrifice of accuracy. Highly accurate basis sets[29] and robust Born-Oppenheimer MD[16, 19] can be used throughout. Particularly important in this respect is the series of simulations I have performed on water.[16,17,20,21,22,34,40,46] This significant effort is motivated by the fact that liquid water is the most important liquid on earth, but a the same time a very controversial topic and a delicate liquid to simulate.

The robustness and efficiency of CP2K is recognized by a rapidly growing community of users, resulting in lively feedback, exciting collaborations, and new features. At the time of writing, CP2K based simulations account for approximately 30% of the CPU time on the largest swiss national supercomputer (a ~14'000 cores Cray XT5, at CSCS, Manno), with significant contributions from users from UZH, ETHZ, PSI, and EMPA. CP2K is open source, which results in user-contributed enhancements. A particularly important aspect of the CP2K project is the wide range of methods and techniques that is transparently combinable and interchangeable, which will be a crucial advantage for multiscale developments. For example, DFT, semi-empirical methods and classical force-fields can be combined in various ways, including, but not limited to QM/MM. Our modular approach allows using methods that were initially conceived for classical simulations with QM methods and the other way around. For example the vapor-liquid phase equilibrium of water has been computed using Gibbs ensemble Monte Carlo at the DFT level, but with Monte Carlo moves biased using classical force fields[20,21,22]. As new methods are incorporated, novel combinations can be explored easily.

 

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