Transition-metal complexes are attractive targets for the design of catalysts and functional
materials. The behavior of the metal–organic bond, while very tunable for achieving target …

It is safe to say that every invention that has changed the world has depended on materials.
At present, the demand for the development of materials and the invention or design of new …

The ability to make rapid and accurate predictions on bandgaps of double perovskites is of
much practical interest for a range of applications. While quantum mechanical computations …

We present a machine-learning-augmented chemisorption model that enables fast and
accurate prediction of the surface reactivity of metal alloys within a broad chemical space …

Data-driven approaches are particularly useful for computational materials discovery and
design as they can be used for rapidly screening over a very large number of materials, thus …

We propose a novel representation of materials named an 'orbital-field matrix (OFM)', which
is based on the distribution of valence shell electrons. We demonstrate that this new …

Abstract Machine Learning (ML) plays an increasingly important role in the discovery and
design of new materials. In this paper, we demonstrate the potential of ML for materials …

We propose a novel representation of materials named an 'orbital-field matrix (OFM)', which
is based on the distribution of valence shell electrons. We demonstrate that this new …

We have developed a descriptor named Orbital Field Matrix (OFM) for representing material
structures in datasets of multi-element materials. The descriptor is based on the information …

We present a data-driven approach to predict entropy changes (ΔS) in small magnetic fields
in single-molecule magnets (SMMs) relevant to their application as magnetocaloric …