Solving the multi-scale problem in materials mechanics: a pathway to chemical design

Introduction

Based on recent conceptual and theoretical advances in the lattice dynamics of real solids, I aim to develop a new generation of computational methods that will revolutionize the way we predict and describe the mechanical response of complex materials.

Current Methods

State-of-the-art computational methods to simulate materials and their mechanical behavior are based on molecular dynamics (MD) with atomistic force-fields. These methods provide an excellent description of the thermodynamically stable phases of materials with arbitrary chemical and microstructural complexity.

Challenges in Simulation

However, simulating the mechanical deformation behavior of materials at the atomistic level remains an open challenge. The main bottleneck is represented by the inevitably short time scale of time integration (1-2 femtoseconds) in atomistic MD methods.

This limitation makes it impossible to simulate the dynamical deformation of materials on long time scales encountered in experiments, i.e. for deformation rates lower than ~10 Gigahertz (at best). This fundamental time-scale bridging problem is currently unsolved and prevents the computational prediction of materials mechanics in the regimes that are experimentally accessible in standard mechanical tests and rheology.

Proposed Solutions

In this project, I build on my expertise and recent scientific breakthroughs in the lattice dynamics and atomistic viscoelasticity of real complex materials.

I propose to develop:

1. A fully predictive and atomistic computational framework for the viscoelastic response (i.e. viscoelastic moduli) of real materials (polymers, glasses, microstructured crystalline materials) that can work across the whole spectrum of deformation rates/frequencies and for large systems (millions of atoms or more).
2. A predictive lattice-dynamics-based framework for the plasticity and yielding of complex materials, including amorphous materials.

This cannot be done with the current state-of-the-art methodologies.