Top Curr Chem (2012) 307: 1–42 DOI: 10.1007/128_2010_114 # Springer-Verlag Berlin Heidelberg 2011 Published online: 18 January 2011
First-Principles-Based Multiscale, Multiparadigm Molecular Mechanics and Dynamics Methods for Describing Complex Chemical Processes Andres Jaramillo-Botero, Robert Nielsen, Ravi Abrol, Julius Su, Tod Pascal, Jonathan Mueller and William A. Goddard III Abstract We expect that systematic and seamless computational upscaling and downscaling for modeling, predicting, or optimizing material and system properties and behavior with atomistic resolution will eventually be sufficiently accurate and practical that it will transform the mode of development in the materials, chemical, catalysis, and Pharma industries. However, despite truly dramatic progress in methods, software, and hardware, this goal remains elusive, particularly for systems that exhibit inherently complex chemistry under normal or extreme conditions of temperature, pressure, radiation, and others. We describe here some of the significant progress towards solving these problems via a general multiscale, multiparadigm strategy based on first-principles quantum mechanics (QM), and the development of breakthrough methods for treating reaction processes, excited electronic states, and weak bonding effects on the conformational dynamics of large-scale molecular systems. These methods have resulted directly from filling in the physical and chemical gaps in existing theoretical and computational models, within the multiscale, multiparadigm strategy. To illustrate the procedure we demonstrate the application and transferability of such methods on an ample set of challenging problems that span multiple fields, system length- and timescales, and that lay beyond the realm of existing computational or, in some case, experimental approaches, including understanding the solvation effects on the reactivity of organic and organometallic structures, predicting transmembrane protein structures, understanding carbon nanotube nucleation and growth, understanding the effects of electronic excitations in materials subjected to extreme conditions of temperature and pressure, following the dynamics and energetics of long-term conformational evolution of DNA
A. Jaramillo-Botero (*), R. Nielsen, R. Abrol, J. Su, T. Pascal, J. Mueller, and W.A. Goddard III (*) Chemistry and Chemical Engineering, California Institute of Technology, Mail code 139-74, 1200 E California Blvd, Pasadena, CA 91125, USA e-mail:
[email protected],
[email protected]
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macromolecules, and predicting the long-term mechanisms involved in enhancing the mechanical response of polymer-based hydrogels.
Keywords Multiscale modeling, Nanotube growth, Non-adiabatic molecular dynamics, Organometallic structures, Protein structure prediction, Reactive molecular dynamics
Contents 1 2
First Principles-Based Multiscale, Multiparadigm Simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 The Role of QM in Multiscale Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2.1 The Wave Equation for Matter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2.2 Approximations to Schr€ odinger’s Equation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 3 From QM to Molecular Mechanics/Dynamics: Force Fields . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 3.1 Conventional Force Fields . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 3.2 Simulating Complex Chemical Processes with FFs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 4 Bridging MM/MD with the Mesoscale . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 4.1 Constrained and Coarse-Grain MD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 5 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
1 First Principles-Based Multiscale, Multiparadigm Simulations The computations required for accurate modeling and simulation of large-scale systems with atomistic resolution involve a hierarchy of levels of theory: quantum mechanics (QM) to determine the electronic states; force fields to average the electronics states and to obtain atom based forces (FF), molecular dynamics (MD) based on such an FF; mesoscale or coarse grain descriptions that average or homogenize atomic motions; and finally continuum level descriptions (see Fig. 1). By basing computations on first principles QM it is possible to overcome the lack of experimental data to carry out accurate predictions with atomistic resolution, which would otherwise be impossible. Furthermore, QM provides the fundamental information required to describe quantum effects, electronically excited states, as well as reaction paths and barrier heights involved in chemical reactions processes. However, the practical scale for accurate QM today is
