Quantum Mechanical Treatment of Variable Molecular Composition: From "Alchemical" Changes of State Functions to Rational Compound Design

TL;DR

This paper explores the use of quantum chemistry and alchemical interpolation paths to predict changes in molecular properties, demonstrating the method's effectiveness in estimating free energy differences and binding potentials.

Contribution

It introduces a quantum mechanical framework for alchemical changes, showcasing predictive power of derivatives for molecular property estimation and screening.

Findings

Alchemical derivatives can predict HOMO eigenvalue changes during nuclear charge transformations.

First-order derivatives accurately estimate covalent and van der Waals interactions.

Alchemical methods enable efficient screening of binding potentials with reasonable accuracy.

Abstract

"Alchemical" interpolation paths, i.e.~coupling systems along fictitious paths that without realistic correspondence, are frequently used within materials and molecular modeling and simulation protocols for the estimation of relative changes in state functions such as free energies. We discuss alchemical changes in the context of quantum chemistry, and present illustrative numerical results for the changes of HOMO eigenvalues of the He atom due to a linear alchemical teleportation---the simultaneous annihilation and creation of nuclear charges at different locations. To demonstrate the predictive power of alchemical first order derivatives (Hellmann-Feynman) the covalent bond potential of hydrogen fluoride and hydrogen chloride is investigated, as well as the van-der-Waals binding in the water-water and water-hydrogen fluoride dimer, respectively. Based on converged electron densities for one configuration, the versatility of alchemical derivatives is exemplified for the screening of entire binding potentials with reasonable accuracy. Finally, we discuss constraints for the identification of non-linear coupling potentials for which the energy's Hellmann-Feynman derivative will yield accurate predictions.