Reliable computational design of biological-inorganic materials to the large nanometer scale using Interface-FF

Abstract

The function of nanomaterials and biomaterials greatly depends on understanding nanoscale recognition mechanisms, crystal growth and surface reactions. The Interface Force Field (IFF) and surface model database are the first collection of transferable parameters for inorganic and organic compounds that can be universally applied to all materials. IFF uses common energy expressions and achieves best accuracy among classical force fields due to rigorous validation of structural and energetic properties of all compounds in comparison to perpetually valid experimental data. This paper summarises key aspects of parameterisation, including atomic charges and transferability of parameters and current coverage. Examples of biomolecular recognition at metal and mineral interfaces, surface reactions of alloys, as well as new models for graphitic materials and pi-conjugated molecules are described. For several metal–organic interfaces, a match in accuracy of computed binding energies between of IFF and DFT results is demonstrated at ten million times lower computational cost. Predictive simulations of biomolecular recognition of peptides on phosphate and silicate surfaces are described as a function of pH. The use of IFF for reactive molecular dynamics is illustrated for the oxidation of Mo₃Si alloys at high temperature, showing the development of specific porous silica protective layers. The introduction of virtual pi electrons in graphite and pi-conjugated molecules enables improvements in property predictions by orders of magnitude. The inclusion of such molecule-internal polarity in IFF can reproduce cation–pi interactions, pi-stacking in graphite, DNA bases, organic semiconductors and the dynamics of aqueous and biological interfaces for the first time.

Keywords:

• Molecular dynamics
• force fields
• metals
• minerals
• graphite
• pH
• proteins

Acknowledgements

JM acknowledges the help from M. Taylor, J. Perepezko and G. A. Melinte. The allocation of computational resources at the CU Biofrontiers Computing Cluster and at the Ohio Supercomputing Center is acknowledged.