Ab initio guided atomistic modelling of nanomaterials on exascale high-performance computing platforms

Abstract

The continuous development of increasingly powerful supercomputers makes theory-guided discoveries in materials and molecular sciences more achievable than ever before. On this ground, the incoming arrival of exascale supercomputers (running over 10^18 floating point operations per second) is a key milestone that will tremendously increase the capabilities of high-performance computing (HPC). The deployment of these massive platforms will enable continuous improvements in the accuracy and scalability of ab initio codes for materials simulation. Moreover, the recent progress in advanced experimental synthesis and characterisation methods with atomic precision has led ab initio-based materials modelling and experimental methods to a convergence in terms of system sizes. This makes it possible to mimic full-scale systems in silico almost without the requirement of experimental inputs. This article provides a perspective on how computational materials science will be further empowered by the recent arrival of exascale HPC, going alongside a mini-review on the state-of-the-art of HPC-aided materials research. Possible challenges related to the efficient use of increasingly larger and heterogeneous platforms are commented on, highlighting the importance of the co-design cycle. Also, some illustrative examples of materials for target applications, which could be investigated in detail in the coming years based on a rational nanoscale design in a bottom-up fashion, are summarised.