<i>Ab inito</i> quantum chemistry (QC) enables the calculation of chemical properties from first principles without the need to parameterize interactions with empirical data. This flexibility is limited by computational resource requirements (CPU and memory) that grow nonlinearly with molecular size, putting an effective upper bound on system size that ranges from tens to hundreds of atoms, depending on methodology. Fragment-based QC approaches sidestep this nonlinearity by dividing large systems into smaller subsystems (fragments) whose properties can be computed efficiently via distributed computing, then combined to approximate an otherwise intractable calculation. The large number of subsystem calculations introduces software complexity that we have addressed by means of novel energy-based screening algorithms, which manage the complexity by limiting the calculation to essential fragments in an automated way. These strategies have solved the challenging problem of predicting relative energies of different proton orderings in water cluster isomers, and have also been used to compute thermochemical properties for metalloenzymes at reasonable cost and with sub-kcal/mol accuracy. In the future, fragmentation techniques could pave the way toward large-scale <i>ab initio</i> molecular dynamics simulations.

Quantum chemistry in large systems with fragmentation

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