Computational materials science, leveraging ab initio simulations and high-performance computing, is poised to play a pivotal role in realising the vision of "materials by design". However, the goal of discovering game-changing materials with significant scientific and industrial relevance demands highly accurate ab initio methods capable of addressing both excited state and ground state properties of atoms, molecules, and solids. To date, the computational complexity of these methods has meant that approaches with systematically improvable accuracy for condensed matter systems—such as coupled-cluster (CC) theories—are primarily confined to studying ground state properties within the clamped-nuclei approximation.
This project aims to induce a paradigm shift in how we study the vibrational and optical properties of real materials by introducing a series of novel methodologies. On one hand, we propose to reduce the computational cost of time-dependent equation-of-motion coupled-cluster (TD-EOM-CC) theory compared to existing approaches, making it feasible to study larger and more complex systems. On the other hand, we aim to implement coupled-cluster atomic forces and combine them with the framework of machine-learning force fields.
Together, these proposed methods have the potential to achieve an unprecedented level of accuracy and scalability for the prediction of a wide range of material properties, including optical spectra and phonon frequencies. By leveraging these new approaches, we aim to resolve several long-standing discrepancies between theoretical predictions and experimental data, particularly for the dynamic properties of defects, molecular crystals, and layered materials.
