Nanoscale Simulations

QM and MM beyond QM/MM

QM and MM beyond QM/MM is an important idea behind some of my work. Indeed, various combinations are possible to extend the reach of either method. A simple idea, but highly efficient approach, is to generate configurations (sample) using a cheap classical model, and compute properties on the basis of these configurations. I demonstrated this for the redox properties of small molecules in solution[24] and proteins[26]. In this case, the quality of the model used for generating the configurations influences the final answer. If this is not desired, it is possible to unbias the classical sampling. In this way, the exact result is ultimately obtained, even though the rate of convergence depends on the quality of the classical model. This was shown in our early work on ab initio free energy profiles[2], and later for water's vapor-liquid equilibrium[20,21,22] computed using Monte Carlo. It is even possible to retain the correct time evolution of a system by combining expensive and cheap models in a multiple-timestep approach. I have shown this for a combination of hybrid and local DFT.[34] Another strategy is to improve the accuracy of a quantum approach, by correcting it with a classical method. This has become established for dispersion (DFT-D), and we have confirmed that this approach yields an improved model for the structure (density) of liquid water.[40] Finally, QM calculations can be used to provide feedback for an MM model, and accurate MM models can be obtained matching forces and energies obtained from QM.[31]


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