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

Dharmawardhana, Chamila C.; Kanhaiya, Krishan; Lin, Tzu-Jen; Garley, Amanda; Knecht, Marc R.; Zhou, Jihan; Miao, Jianwei; Heinz, Hendrik

doi:10.1080/08927022.2017.1332414

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

Journal Article · Fri May 19 00:00:00 EDT 2017 · Molecular Simulation

DOI:https://doi.org/10.1080/08927022.2017.1332414· OSTI ID:1565598

Dharmawardhana, Chamila C. ^[1]; Kanhaiya, Krishan ^[1]; Lin, Tzu-Jen ^[2]; Garley, Amanda ^[1]; Knecht, Marc R. ^[3]; Zhou, Jihan ^[4]; Miao, Jianwei ^[4]; Heinz, Hendrik ^[1]

Department of Chemical and Biological Engineering, University of Colorado at Boulder, Boulder, CO, USA
Department of Chemical Engineering, Chung Yuan Christian University, Taoyuan City, Taiwan, ROC
Department of Chemistry, University of Miami, Coral Gables, FL, USA
Department of Physics and Astronomy and California NanoSystems Institute, University of California, Los Angeles, CA, USA

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.

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Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF); Argonne National Laboratory (ANL), Argonne, IL (United States); UT-Battelle LLC/ORNL, Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE Office of Science (SC)

DOE Contract Number:: AC02-06CH11357; AC05-00OR22725

OSTI ID:: 1565598

Journal Information:: Molecular Simulation, Vol. 43, Issue 13-16; ISSN 0892-7022

Publisher:: Taylor & Francis

Country of Publication:: United States

Language:: English

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