Surfing multiple conformation-property landscapes via machine learning: Designing magnetic anisotropy

TL;DR

This paper demonstrates a machine learning approach combined with optimization algorithms to automatically design molecular structures with enhanced magnetic anisotropy, enabling efficient exploration of chemical space for magnetic materials.

Contribution

It introduces a novel machine learning framework integrated with particle-swarm optimization for reverse engineering magnetic properties of molecular magnets.

Findings

A 5% change in coordination angle increases anisotropy by 50%.

The method can predict energy and magnetic properties from structure.

It enables automated design of new magnetic materials.

Abstract

The advent of computational statistical disciplines, such as machine learning, is leading to a paradigm shift in the way we conceive the design of new compounds. Today computational science does not only provide a sound understanding of experiments, but also can directly design the best compound for specific applications. This approach, known as reverse engineering, requires the construction of models able to efficiently predict continuous structure-property maps. Here we show that reverse engineering can be used to tune the magnetic properties of a single-ion molecular magnet in an automated intelligent fashion. We design a machine learning model to predict both the energy and magnetic properties as function of the chemical structure. Then, a particle-swarm optimization algorithm is used to explore the conformational landscapes in the search for new molecular structures leading to an enhanced magnetic anisotropy. We find that a 5% change in one of the coordination angles leads to a 50% increase in the anisotropy. Our approach paves the way for a machine-learning-driven exploration of the chemical space of general classes of magnetic materials. Most importantly, it can be applied to any structure-property relation and offers an effective way to automatically generate new materials with target properties starting from the knowledge of previously synthesized ones.