820 citations · 2.1k across the 9 of their papers we have counts for
9 papers
Modeling high-entropy transition-metal alloys with alchemical compression
Nataliya Lopanitsyna, Guillaume Fraux, Maximilian A. Springer +2
Alloys composed of several elements in roughly equimolar composition, often referred to as high-entropy alloys, have long been of interest for their thermodynamics and peculiar mec…
An assessment of the structural resolution of various fingerprints commonly used in machine learning
Behnam Parsaeifard, Deb Sankar De, Anders S. Christensen +6
Atomic environment fingerprints are widely used in computational materials science, from machine learning potentials to the quantification of similarities between atomic configurat…
Chemical Shifts in Molecular Solids by Machine Learning
Federico M. Paruzzo, Albert Hofstetter, Félix Musil +3
The calculation of chemical shifts in solids has enabled methods to determine crystal structures in powders. The dependence of chemical shifts on local atomic environments sets the…
Machine Learning Unifies the Modelling of Materials and Molecules
Albert P. Bartok, Sandip De, Carl Poelking +4
Determining the stability of molecules and condensed phases is the cornerstone of atomistic modelling, underpinning our understanding of chemical and materials properties and trans…
Mapping and Classifying Molecules from a High-Throughput Structural Database
Sandip De, Felix Musil, Teresa Ingram +2
High-throughput computational materials design promises to greatly accelerate the process of discovering new materials and compounds, and of optimizing their properties. The large…
Comparing molecules and solids across structural and alchemical space
Sandip De, Albert P. Bartók, Gábor Csányi +1
Evaluating the (dis)similarity of crystalline, disordered and molecular compounds is a critical step in the development of algorithms to navigate automatically the configuration sp…