First principles view on chemical compound space: Gaining rigorous atomistic control of molecular properties

TL;DR

This paper reviews an atomistic first principles approach to chemical compound space, discussing variational nuclear charges, alchemical interpolations, property non-linearity, and machine learning methods for predicting molecular properties.

Contribution

It provides a comprehensive first principles perspective on chemical compound space, integrating variational theories, alchemical interpolations, and machine learning for property prediction.

Findings

Alchemical interpolations enable fractional nuclear charge variations.

Taylor expansions help analyze property non-linearity.

Machine learning models infer structure-property relationships.

Abstract

A well-defined notion of chemical compound space (CCS) is essential for gaining rigorous control of properties through variation of elemental composition and atomic configurations. Here, we review an atomistic first principles perspective on CCS. First, CCS is discussed in terms of variational nuclear charges in the context of conceptual density functional and molecular grand-canonical ensemble theory. Thereafter, we revisit the notion of compound pairs, related to each other via "alchemical" interpolations involving fractional nuclear chargens in the electronic Hamiltonian. We address Taylor expansions in CCS, property non-linearity, improved predictions using reference compound pairs, and the ounce-of-gold prize challenge to linearize CCS. Finally, we turn to machine learning of analytical structure property relationships in CCS. These relationships correspond to inferred, rather than derived through variational principle, solutions of the electronic Schr\"odinger equation.