Computational needs of quantum mechanical calculations of materials for high-energy physics

TL;DR

This paper discusses the importance and computational requirements of quantum mechanical calculations for materials used in high-energy physics, highlighting current methods and future directions including machine learning and quantum computing.

Contribution

It provides an overview of the scientific need, computational challenges, and future prospects for ab initio materials calculations in high-energy physics applications.

Findings

Assessment of computational resources needed for ab initio calculations

Identification of workflows used in current state-of-the-art methods

Future directions include machine learning and quantum computing approaches

Abstract

Searches for new physics in high-energy physics (HEP) experiments commonly rely on interactions with materials. A burgeoning direction is the accurate calculation and design of materials for HEP applications. In this Snowmass contribution, I briefly motivate the science need for quantum mechanical calculations of materials for HEP and outline the range of questions that such calculations can address. With this information, I assess the computational needs for ab initio calculations in HEP, the specific computational resources and workflows used by state-of-the-art methods, and finally identify promising future directions such as the use of machine learning and strongly-correlated quantum mechanical calculations moving towards materials calculations on quantum computers.