Til bevillingsoversigt

DECODENSE: Mean-Field Density Matrix Decompositions

Reintegration Fellowships


The main objective of the DECODENSE project is the development of new, rapid simulation tools in the field of quantum chemistry. In particular, I will be concerned with the acceleration of contemporary, state-of-the-art electronic structure methods by means of modern machine learning. Through the formulation of novel theory and its efficient implementation within optimized computational workflows, I am proposing new manners by which to decompose quantum-chemical simulations. In turn, these decompositions will make the underlying theory increasingly befitting to the deployment of machine learning in inferring physical properties from chemical structures alone. Importantly, these mappings will be made without recourse to explicit and exceedingly expensive electronic structure treatments.


The inner workings of molecules and materials may these days be probed and modelled by advanced simulation tools on modern computer architectures. These simulations not only serve as a complement to traditional empirical explorations, but also as practical extensions in cases where these prove infeasible, hazardous, or exceedingly expensive. The crux of the matter, however, is the fact that today's primary computational workhorse - known as Kohn-Sham density functional theory (KS-DFT) - is inherently too computationally costly to warrant large-scale simulations of, e.g., potential drug prospects or emerging solid-state batteries. To that end, my project will ultimately be concerned with circumventing the prohibitive scaling wall of KS-DFT by deploying machine learning to the problem.


A Carlsberg Foundation Reintegration Fellowship will enable me to return home to Denmark after two successful postdoctoral stays abroad. The fellowship will see me integrate into one of our country's most vibrant and interdisciplinary academic environments at DTU Chemistry, which is certain to offer me both the necessary support and the scientific independence to drive my ongoing endeavours towards the development of more accurate takes on machine-learned quantum chemistry to new levels. With a sound core of theory development and high-performance computing in roughly equal parts, my project is bound to offer a manifold of physical and chemical insight as well as a firm basis for forming my own research lab at DTU in the years to come.


The DECODENSE project has as its primary ambition to provide clues and answers to some of today's most pressing grand challenges in the domain of physical chemistry, such as, which compounds in chemical space exhibit a particular key property? Or how is chemical energy converted to electrical energy at a detailed atomistic level in next-generation battery candidates? These complex, yet stimulating societal problems will all benefit from the availability of accelerated, machine-learned simulation tools, which can enter into high-throughput screening and inverse design protocols aimed at exploring the functional materials of tomorrow. Due to its versatility, the outcomes of my project are fully intended to add utility value beyond the niche of fundamental basic science.