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Structural transitions in dense disordered silicon from quantum-accurate ultra-large-scale simulations
Volker L. Deringer, Noam Bernstein, Gábor Csányi +3
Structurally disordered materials continue to pose fundamental questions, including that of how different disordered phases ("polyamorphs") can coexist and transform from one to an…
Impact of biaxial and uniaxial strain on VO
Darshana Wickramaratne, Noam Bernstein, I. I. Mazin
Using first-principles calculations we determine the role of compressive and tensile uniaxial and equibiaxial strain on the structural, electronic and magnetic properties of VO…
Machine-learned Interatomic Potentials for Alloys and Alloy Phase Diagrams
Conrad W. Rosenbrock, Konstantin Gubaev, Alexander V. Shapeev +4
We introduce machine-learned potentials for Ag-Pd to describe the energy of alloy configurations over a wide range of compositions. We compare two different approaches. Moment tens…
De novo exploration and self-guided learning of potential-energy surfaces
Noam Bernstein, Gábor Csányi, Volker L. Deringer
Interatomic potential models based on machine learning (ML) are rapidly developing as tools for materials simulations. However, because of their flexibility, they require large fit…
The role of defects in the metal-insulator transition in VO and VO
Darshana Wickramaratne, Noam Bernstein, I. I. Mazin
The vanadates VO and VO are prototypical examples of strongly correlated materials that exhibit a metal-insulator transition. While the phase transitions in these mater…
Quantifying Chemical Structure and Atomic Energies in Amorphous Silicon Networks
Noam Bernstein, Bishal Bhattarai, Gábor Csányi +3
Amorphous materials are coming within reach of realistic computer simulations, but new approaches are needed to fully understand their intricate atomic structures. Here, we show ho…