Strategies for multiscale modeling and simulation of organic materials: polymers and biopolymers

doi:10.1016/S1089-3156(01)00025-3

Computational and Theoretical Polymer Science

Volume 11, Issue 5, October 2001, Pages 329-343

https://doi.org/10.1016/S1089-3156(01)00025-3 Get rights and content

Abstract

Advances in theory and methods are making it practical to consider fully first principles (de novo) predictions of structures, properties and processes for organic materials. However, despite the progress there remains an enormous challenge in bridging the vast range of distances and time scales between de novo atomistic simulations and the quantitative continuum models for the macroscopic systems essential in industrial design and operations. Recent advances relevant to such developments include: quantum chemistry including continuum solvation and force field embedding, de novo force fields to describe phase transitions, molecular dynamics (MD) including continuum solvent, non equilibrium MD for rheology and thermal conductivity and mesoscale simulations. To provide some flavor for the opportunities we will illustrate some of the progress and challenges by summarizing some recent developments in methods and their applications to polymers and biopolymers. Four different topics will be covered: (1) hierarchical modeling approach applied to modeling olfactory receptors, (2) stabilization of leucine zipper coils by introduction of trifluoroleucine, (3) modeling response of polymers sensors for electronic nose, and (4) diffusion of gases in amorphous polymers.

Introduction

In order to develop new materials and composites with designed new properties, it is essential that these properties be predicted before preparation, processing, and experimental characterization. Despite the tremendous advances made in the modeling of the structural, thermal, mechanical and transport properties of materials at the macroscopic level (finite element analysis of complicated structures) there remains tremendous uncertainty about how to predict many critical properties related to performance. The fundamental problem here is that these properties depend on the atomic level interactions and chemistry (e.g. making and breaking of bonds) dealing with the electronic and atomic level description at the level of nanometers and picoseconds. The materials designer needs answers from macroscopic modeling (finite element paradigm) of components having scales of centimeters and milliseconds or larger. To dramatically advance the ability to design useful high performance materials, it is essential that we insert the chemistry into the mesoscopic and macroscopic (finite element) modeling.

The difficulties in doing this are shown in Fig. 1, where we see that vast length and time scales separate the quantum mechanics (QM) from the macroscopic world of engineering design. Tremendous advances have been made recently in first principles QM predictions of chemical reactions, but the state of the art can handle accurately reactions with only ∼50 atoms. There is no practical approach to carrying out a QM calculation on the initiation and propagation of a crack through a stabilized zirconia ceramic. Despite this difficulty, the computations MUST be based on accurate first-principles QM if we are to predict the properties of new materials.

Our strategy for accomplishing this objective is to develop an overlapping array of successively coarser modeling techniques. At each plateau (a range of length and time scales), the parameters of the coarse description are based on the parameters of the immediately finer description, as shown in Fig. 1. Thus based on accurate QM calculations we find a force field (FF) including charges, force constants, polarization, van der Waals interactions etc that accurately reproduces the QM. With the FF, the dynamics is described with Newton's equations [molecular dynamics (MD)], instead of the Schrodinger Equation.

The MD level allows one to predict the structures and properties for systems ∼10⁵ times larger than for QM, allowing direct simulations for the properties of many interesting systems. This leads to many results relevant and useful in materials design, however, many critical problems in materials design require time and length scales far too large for practical MD.

Thus we need to develop methods treating the mesoscale in between the atomic length and time scales of MD and the macroscopic length and time scales (microns to mm and μs to s) of finite element analysis (FEA). This linking through the mesoscale in which we can describe microstructure is probably the greatest challenge to developing reliable first principles methods for practical materials' design applications.

Only by establishing this connection from microscale to mesoscale it is possible to build first principles methods for describing the properties of new materials and composites. Our aim is to reach the domain of materials science and engineering by building from fundamental principles of physics and chemistry. Thus, for fundamental predictions to play a direct role in materials innovation and design, it is essential to bridge the micro–meso gap. The problem here is that the methods of coarsening the description from atomistic to mesoscale or mesoscale to continuum is not so obvious as it was in going from electrons to atoms. For example, the strategy for polymers seems quite different than for metals, which seem different from ceramics or semiconductors.

Given the concepts, it is necessary to carry out calculations for realistic time scales fast enough to be useful in design. This requires developing software tools useful by design engineers, by incorporating the methods and results of the QM to MD to mesoscale simulations. To accomplish the goals of developing methods for accurate calculations of materials and properties, we focus on: (i) implementations that make full use of modern highly parallel computers, and (ii) building in knowledge based heuristic methods of accessing this information automatically so that designers can focus on the macroscopic issues without concern for the details of theory and simulation. At this point, we expect a revolution in materials design and innovations where the first-principles multiscale modeling and simulations play increasing role in the design stage and complementing the experiments.

Section snippets

Progress in methods developments

Our strategy is to transcend from the most fundamental theory (QM) to practical engineering designs in a sequence of several levels as indicated in Fig. 1.

Multiscale modeling of biopolymers

Multi-scale modeling techniques are very vital to research problems in biology. From the fine level calculation of accurate binding energies for drug molecules using QM to coarse level structure prediction for proteins (both globular and membrane) and understanding viral protein coat assembly are some of the typical cases that require multi-scale modeling for biological systems. Such modeling schemes are also critical in bioengineering problems that offer excellent control in growth of a

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Strategies for multiscale modeling and simulation of organic materials: polymers and biopolymers

Abstract

Introduction

Section snippets

Progress in methods developments

Multiscale modeling of biopolymers

Spectrosc Acta A

Chem Phys Lett

J Noncrystal Solids

J Chem Phys

Chem Phys Lett

Adv Eng Software

Fluid Phase Equil

J Mol Biol

Cell

Accts Chem Res

J Am Chem Soc

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Phys Rev B

J Chem Phys

J Phys Chem

J Am Chem Soc

Macromolecules

Macromolecules

J Phys Chem

J Phys Chem

J Phys Chem

J Phys Chem

J Am Chem Soc

MRS Symp Proc

Phys Rev Lett

Comput Mat Sci

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Theo Chem Acct

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J Comp Chem

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J Am Chem Soc

Proteins

J Phys Chem

Mat Res Soc Symp Proc

Comp Polym Sci