Alchemical normal modes unify chemical space
Stijn Fias, K. Y. Samuel Chang, O. Anatole von Lilienfeld

TL;DR
This paper introduces alchemical normal modes (ANMs), a perturbation theory-based method that unifies chemical space representations, enabling efficient exploration and prediction of molecular and material properties across diverse chemical systems.
Contribution
The paper presents a novel ANM framework that unifies coordinate and composition space, facilitating rapid property predictions and compound exploration in high-dimensional chemical space.
Findings
ANMs effectively predict energetics of various diatomic and polyatomic systems.
ANMs enable large-scale screening of BN-doped derivatives with high accuracy.
The approach reduces computational costs in chemical space exploration.
Abstract
In silico design of new molecules and materials with desirable quantum properties by high-throughput screening is a major challenge due to the high dimensionality of chemical space. To facilitate its navigation, we present a unification of coordinate and composition space in terms of alchemical normal modes (ANMs) which result from second order perturbation theory. ANMs assume a predominantly smooth nature of chemical space and form a basis in which new compounds can be expanded and identified. We showcase the use of ANMs for the energetics of the iso-electronic series of diatomics with 14 electrons, BN doped benzene derivatives (C(BN)H with ), predictions for over 1.8 million BN doped coronene derivatives, and genetic energy optimizations in the entire BN doped coronene space. Using Ge lattice scans as reference, the applicability ANMs across the…
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Taxonomy
TopicsMachine Learning in Materials Science · Advanced Chemical Physics Studies · Computational Drug Discovery Methods
