The field of data-driven chemistry is undergoing an evolution, driven by innovations in
machine learning models for predicting molecular properties and behavior. Recent strides in …

In the past decade, computational tools have become integral to catalyst design. They
continue to offer significant support to experimental organic synthesis and catalysis …

Understanding lanthanide coordination chemistry can help develop new ligands for more
efficient separation of lanthanides for critical materials needs. The Cambridge Structural …

Efficient dispersion corrections are an indispensable component of modern density
functional theory, semi-empirical quantum mechanical, and even force field methods. In this …

Organometallic complexes are ubiquitous in numerous technological applications, and in
particular in homogeneous catalysis. Optimization of such complexes for specific …

Transferability, especially in the context of model generalization, is a paradigm of all
scientific disciplines. However, the rapid advancement of machine learned model …

The unique properties of lanthanoids and their diverse applications make them an
indispensable part of modern research and industry. While the field has garnered attention …

The lanthanide elements are crucial components in numerous technologies, yet their
industrial production through liquid–liquid extraction continues to be economically and …

Computational chemistry pipelines typically commence with geometry generation, well-
established for organic compounds but presenting a considerable challenge for transition …

Molecular conformer generation (MCG) is an important task in cheminformatics and drug
discovery. The ability to efficiently generate low-energy 3D structures can avoid expensive …