A quantum chemical approach to dynamic properties of real materials

Introduction

Computational materials science, using ab initio simulations and high-performance computing, is expected to play a key role in realizing the vision of ‘materials by design’. However, the goal to discover game-changing materials with scientific and industrial relevance requires highly accurate ab initio methods for excited state as well as ground state properties of atoms, molecules, and solids.

Current Limitations

So far, due to the computational complexities involved, methods with systematically improvable accuracy for condensed matter systems, such as coupled-cluster theories, are mostly limited to the study of ground state properties in the clamped-nuclei approximation.

Proposal Overview

This ambitious proposal aims at inducing a computational paradigm shift in the study of vibrational and optical properties of real materials by implementing a multitude of novel methods.

Cost Reduction

On the one hand, we propose to reduce the computational cost of time-dependent equation-of-motion coupled-cluster theory by several orders of magnitude compared to existing approaches.

Machine Learning Integration

On the other hand, coupled-cluster atomic forces will be implemented for machine-learning force fields in the Gaussian approximation potentials framework.

Expected Outcomes

Together, the proposed methods have the potential to achieve an unprecedented level of accuracy and system size for the prediction of a wide range of material properties including:

1. Optical spectra
2. Phonon frequencies

Research Goals

We seek to employ the newly developed approaches to resolve a number of long-standing discrepancies between theoretical predictions and experimental findings for dynamic properties of:

• Defects
• Molecular crystals
• Layered materials

These carefully selected systems highlight key problems of currently available ab initio methods. Novel approaches that go beyond the state of the art will have an enormous impact in all areas of physics, chemistry, and computational materials science.