103 citations · 133 across the 7 of their papers we have counts for
6 papers · 1 filter
Introducing physics-informed generative models for targeting structural novelty in the exploration of chemical space
Andrij Vasylenko, Federico Ottomano, Christopher M. Collins +3
Discovering materials with new structural chemistry is key to achieving transformative functionality. Generative artificial intelligence offers a scalable route to propose candidat…
Learning Atoms from Crystal Structure
Andrij Vasylenko, Dmytro Antypov, Sven Schewe +4
Computational modelling of materials using machine learning, ML, and historical data has become integral to materials research. The efficiency of computational modelling is strongl…
Inferring energy-composition relationships with Bayesian optimization enhances exploration of inorganic materials
Andrij Vasylenko, Benjamin Asher, Chris C. Collins +4
Computational exploration of the compositional spaces of materials can provide guidance for synthetic research and thus accelerate the discovery of novel materials. Most approaches…
Element selection for functional materials discovery by integrated machine learning of elemental contributions to properties
Andrij Vasylenko, Dmytro Antypov, Vladimir Gusev +3
Fundamental differences between materials originate from the unique nature of their constituent chemical elements. Before specific differences emerge according to the precise ratio…
Single-Atom Scale Structural Selectivity in Te Nanowires Encapsulated inside Ultra-Narrow, Single-Walled Carbon Nanotubes
Paulo V. C. Medeiros, Samuel Marks, Jamie M. Wynn +5
Extreme nanowires (ENs) represent the ultimate class of crystals: They are the smallest possible periodic materials. With atom-wide motifs repeated in one dimension (1D), they offe…
Encapsulated Nanowires: Boosting Electronic Transport in Carbon Nanotubes
Andrij Vasylenko, Jamie Wynn, Paulo Medeiros +3
The electrical conductivity of metallic carbon nanotubes (CNTs) quickly saturates with respect to bias voltage due to scattering from a large population of optical phonons. Decay o…