English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

The advent of computational statistical disciplines, such as machinelearning, is leading to a paradigm shift in the way we conceive the design ofnew compounds. Today computational science does not only provide a soundunderstanding of experiments, but also can directly design the best compoundfor specific applications. This approach, known as reverse engineering,requires the construction of models able to efficiently predict continuousstructure-property maps. Here we show that reverse engineering can be used totune the magnetic properties of a single-ion molecular magnet in an automatedintelligent fashion. We design a machine learning model to predict both theenergy and magnetic properties as function of the chemical structure. Then, aparticle-swarm optimization algorithm is used to explore the conformationallandscapes in the search for new molecular structures leading to an enhancedmagnetic anisotropy. We find that a 5% change in one of the coordination anglesleads to a 50% increase in the anisotropy. Our approach paves the way for amachine-learning-driven exploration of the chemical space of general classes ofmagnetic materials. Most importantly, it can be applied to anystructure-property relation and offers an effective way to automaticallygenerate new materials with target properties starting from the knowledge ofpreviously synthesized ones.

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