Turning gold standard quantum chemistry into a routine simulation tool: predictive properties for large molecular systems

We propose comprehensive theoretical method development targeting a long-standing dilemma in molecular quantum simulations between controllable predictive power and affordable computational time.

While theoutstanding reliability of quantum chemistry’s gold standard model is repeatedly corroborated against experiments, its traditional form is limited to the size of an amino acid molecule.

By exploiting the short-range natureof leading interaction contributions, a handful of groups, including ours, have recently extended the reach ofsuch quantitative energy computations up to a few hundred atoms.

However, these state-of-the-art models arestill too demanding and are not at all equipped to compute experimentally relevant dynamic, spectroscopic, andthermodynamic molecular properties.Thus, to break down these barriers, we will further accelerate our cutting-edge gold standard methods upto few 1000 atoms via concerted theoretical and algorithmic developments, and high-performance softwaredesign.

Additionally, we will take into account biochemical, crystal, and solvent environment effects via cost-efficient embedding models.

For the first time, we will also derive and implement practical approaches tocompute static and dynamic observable properties for large molecules at the gold standard level.

The exceptional capabilities of the new methods will enable us to study challenging chemical processesof practical importance which are not accessible with chemical accuracy for any current lower-cost alternative.We aim at modeling and understanding intricate covalent- and non-covalent interactions governing supramolecularand protein-ligand binding as well as the mechanism of organo-, organometallic, surface, and enzyme catalyticreactions.Once successful, this project we will deliver groundbreaking and open access tools for the systematicallyimprovable and predictive quantum simulation of large molecules in realistic conditions and environments.